Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Technology Obsolescence - Task force - Data Specialist (VP1)

Lead, AI & Data Products (Technical)

Customer Experience Director

Manager, Process Engineer (Deposition)

Equipment Maintenance Manager

Senior PCB Component Engineer

Product Lifecycle Supply Chain Project Manager (Optic Product)

Test Development Engineer

Responsibilities: Deploy and Land: <ul><li>Own end-to-end delivery of AI and analytics products at stations in your region.</li><li>Cover discovery, scoping, development, integration, user acceptance testing (UAT), hypercare and rollout.</li><li>Embed with operations.</li><li>Participate in business stand-ups.</li><li>Visit the ramp and cargo floor.</li><li>Understand local data realities rather than making assumptions.</li><li>Ship production code in Python and comparable stacks against live flight, cargo, workforce and equipment data.</li><li>Avoid handing over prototypes for someone else to productionise.</li><li>Scale deployment by reducing the cost and time required to deploy solutions across additional stations.</li><li>Achieve this through reusable integrations, configuration and runbooks.</li></ul> Build on the Platform, Not Around It: <ul><li>Consume data through the semantic layer and governed data platform.</li><li>Avoid parallel pipelines, local definitions and workarounds.</li><li>Apply Centre of Excellence (CoE) patterns and guardrails.</li><li>Follow reference architectures, AI registry, human-in-the-loop controls, access and residency rules.</li><li>Integrate GenAI and ML into workflows.</li><li>Implement RAG and retrieval patterns.</li><li>Implement agentic and tool-use patterns.</li><li>Apply evaluation frameworks.</li><li>Establish observability.</li><li>Maintain production cost control.</li></ul> Make Insight Self-Service: <ul><li>Build data products, not report queues.</li><li>Design governed, reusable datasets and semantic models.</li><li>Enable operations teams, airlines and forwarders to answer their own questions.</li><li>Put insight in front of the customer.</li><li>Deliver station and customer-facing analytics.</li><li>Enable natural-language access to operational data.</li><li>Ensure appropriate access, residency and confidentiality controls.</li><li>Design for the user, not the requester.</li><li>Ensure every product identifies the decision it changes and the person responsible for making it.</li><li>Instrument usage.</li><li>Retire products that are not used.</li><li>Consolidate fragmented reporting rather than extending it.</li></ul> Close the Loop: <ul><li>Feed field learnings back.</li><li>Convert integration problems into hardened patterns for the CoE and requirements for the platform team.</li><li>Own adoption and benefits.</li><li>Instrument deployments.</li><li>Track usage and benefits against the business case.</li><li>Report results accurately.</li><li>Be the technical face to the business and customers.</li><li>Translate between operations, the CoE and platform engineering.</li><li>Success in the First 12 Months</li></ul> Requirements: Experience: <ul><li>6+ years of experience in software or ML engineering.</li><li>Experience must include customer-facing or business-embedded delivery.</li><li>Demonstrated track record of scoping and delivering complex systems end to end in ambiguous, fast-moving environments.</li><li>Ability to remain accountable after go-live.</li><li>Production experience with LLM or ML systems.</li><li>Experience with prompt and agent design.</li><li>Experience with RAG.</li><li>Experience with evaluation frameworks.</li><li>Experience with deployment at scale.</li><li>Understanding of how model behaviour influences product experience.</li><li>Strong Python and API development skills.</li><li>Experience with FastAPI or equivalent.</li><li>Comfortable working across backend and frontend development.</li><li>Ability to build a working system independently.</li><li>Practical experience with Docker.</li><li>Practical experience with Kubernetes.</li><li>Practical experience with CI/CD.</li><li>Practical experience with cloud data platforms.</li><li>Experience integrating batch and real-time data against complex enterprise data sources.</li><li>Strong understanding of analytics.</li><li>Experience with semantic and dimensional modelling.</li><li>Knowledge of modern BI.</li><li>Experience designing data products that non-technical and external users can operate independently.</li><li>Aviation, logistics or complex operations experience is advantageous but not mandatory.</li><li>Willingness to learn operations directly from the operational floor is essential.</li></ul> Qualifications: <ul><li>Bachelors, MSc or PhD in Computer Science, Engineering, Mathematics or a related field.</li></ul>

Responsibilities: <ul><li>Own end-to-end delivery of AI and analytics products at stations in your region: discovery, scoping, build, integration, UAT, hypercare and rollout.</li><li>Embed with the operation. Sit in business stand-ups, walk the ramp and the cargo floor, and specify the local data reality rather than assuming it.</li><li>Ship production code in Python and comparable stacks against live flight, cargo, workforce and equipment data, not prototypes handed to someone else to productionise.</li><li>Scale deployment. Reduce cost and time-to-deploy from station N to station N+1 through reusable integrations, configuration and runbooks.</li><li>Consume through the semantic layer and the governed data platform. No parallel pipelines, no local definitions, no workarounds.</li><li>Apply CoE patterns and guardrails: reference architectures, AI registry, human-in-the-loop controls, access and residency rules.</li><li>Integrate GenAI and ML into workflows: RAG and retrieval, agentic and tool-use patterns, evaluation frameworks, observability and cost control in production.</li><li>Build data products, not report queues. Design- governed, reusable datasets and semantic models that let operations, airlines and forwarders answer their own questions.</li><li>Put insight in front of the customer. Deliver station and customer-facing analytics, including natural-language access to operational data, with the access, residency and confidentiality controls customer data demands.</li><li>Design for the user, not the requester. Every product names the decision it changes and who makes it. Instrument usage, retire what nobody opens, consolidate the long tail rather than extend it.&nbsp;</li><li>Feed the field back. Every integration problem you solve becomes a hardened pattern for the CoE and a requirement for the platform team.</li><li>Own adoption and benefits. Instrument what you deploy, track usage and benefit against the business case, and report it honestly.</li><li>Be the technical face to the business and to customers, translating between operations, the CoE and platform engineering.&nbsp;</li></ul> Requirements:&nbsp; <ul><li>6+ years in software or ML engineering, including customer-facing or business-embedded delivery.</li><li>Track record of scoping and shipping complex systems end to end in ambiguous, fast-moving environments, and staying accountable after go-live.</li><li>Production experience with LLM or ML systems: prompt and agent design, RAG, evaluation frameworks, deployment at scale, and an understanding of how model behaviour shapes product experience.</li><li>Strong Python and API development (FastAPI or equivalent); comfortable across backend and front-end enough to build a working system alone.</li><li>Practical depth in Docker, Kubernetes, CI/CD, and cloud data platforms; batch and real-time integration against messy enterprise sources.</li><li>Fluency in the analytics half of the job: semantic and dimensional modelling, modern BI, and designing data products that non-technical and external users can operate without help.</li><li>Aviation, logistics or complex operations exposure is an advantage, not a prerequisite. Willingness to learn the operation from the floor is not optional.</li><li>Bachelors, MSc or PhD in Computer Science, Engineering, Mathematics or related field.&nbsp;</li></ul>

﻿Responsibilities: Data Collection &amp; Integration: Collect and consolidate data from multiple sources, including: <ul><li>CMDB platforms (e.g., Remedy)</li><li>IT Asset Management (ITAM) systems</li><li>Vendor lifecycle and support data</li><li>Security, vulnerability, and patching sources</li><li>Ensure data consistency and alignment across asset, configuration, and lifecycle repositories</li><li>Validate data accuracy, completeness, and currency</li></ul> Technology Obsolescence Analysis: <ul><li>Identify EOL, EOS, and obsolete technologies across hardware, software, OS, databases, middleware, and cloud services</li><li>Analyze lifecycle risks by technology type, vendor, business unit, and criticality</li><li>Support prioritization of remediation based on risk, business impact, and exposure</li></ul> Reporting &amp; Dashboarding: <ul><li>Design, develop, and maintain interactive dashboards and reports using:</li><li>Power BI / Tableau</li></ul> Create Standard Views Covering: <ul><li>Obsolescence risk heatmaps</li><li>Aging and lifecycle timelines</li><li>Remediation progress and trends</li><li>Compliance and audit‑ready metrics</li><li>Enable self‑service analytics for stakeholders where appropriate</li></ul> CMDB &amp; Asset Management Support: <ul><li>Support the alignment of CMDB data with actual deployed assets</li><li>Identify gaps, redundancies, or inaccuracies in configuration and asset records</li><li>Drive improvements in asset lifecycle tagging, ownership, and criticality classification</li><li>Collaborate with ITSM, ITAM, and platform teams to improve data quality and governance</li></ul> Stakeholder &amp; Decision Support: <ul><li>Provide insights and analysis to:</li><li>Domain leads and task force leads</li><li>Project managers and delivery teams</li><li>Risk, security, and audit stakeholders</li><li>Translate complex data into clear, actionable insights for non‑technical audiences</li><li>Support business case development and roadmap planning with data evidence</li></ul> Data Governance &amp; Continuous Improvement: <ul><li>Define and document data definitions, metrics, and reporting standards</li><li>Automate data refreshes and reporting pipelines where possible</li><li>Identify opportunities to improve lifecycle visibility and predictive risk analysis</li></ul> Requirements: <ul><li>5–8+ years of experience in a data analyst or reporting role within IT or technology functions</li><li>Experience supporting modernization, risk, lifecycle, or transformation initiatives is a strong advantage</li><li>Bachelor’s degree in Information Systems, Computer Science, Data Analytics, or related field</li><li>Power BI or Tableau certification</li><li>ITIL or IT Asset Management certification is a plus</li><li>Data &amp; Analytics Skills</li><li>Strong experience in data analysis and visualization</li><li>Hands on expertise in:</li><li>Power BI (DAX, data modeling, performance optimization)</li><li>Tableau (dashboards, calculated fields, storytelling)</li><li>Strong SQL skills for querying large datasets</li><li>Experience working with structured and semi structured data</li></ul>

Role Description: <ul><li>The organization is investing in a small number of high-impact AI and data products intended to operate across its global station network. </li><li>Each requires a single accountable owner who can hold the technical architecture and the product direction together, from first implementation through multi-station scale.</li><li>We are looking for a Lead, AI &amp; Data Products to take that ownership.&nbsp;</li><li>This is a senior individual contributor role for an engineer or applied scientist who has moved into product leadership, not a product manager with adjacent technical exposure.</li><li>You will be expected to reason about model design, data architecture and production trade-offs at the level of the engineers building the system, and to hold the roadmap, business case and stakeholder relationships at the level of the executives funding it.&nbsp;</li><li>The role operates horizontally.</li><li>You will act as the single-threaded owner for assigned initiatives, working with the AI Centre of Excellence on patterns and feasibility, with Data Engineering and Platform on the data foundation, with Operational Excellence and the business product owners on process change, and with the forward deployed engineers in region on station rollout.</li></ul> This is a hands-on role. <ul><li>You will review and contribute to code, make architectural calls, and spend meaningful time at the frontline understanding operations directly rather than through documentation.</li></ul> Responsibilities: Technical product ownership <ul><li>Own the product end to end for assigned AI and data initiatives: discovery, technical architecture, roadmap, delivery sequencing and global rollout readiness.</li><li>Act as single-threaded owner. One person accountable for whether the product works, is adopted, and delivers its business case across stations and regions.</li><li>Convert programs into products.&nbsp;</li><li>Move initiatives currently run as projects onto a product footing with a roadmap, a lifecycle, named users and a benefit measure.</li><li>Make the build decisions: in-house, CoE pattern, capability center, or vendor. Own the trade-off and the consequences.</li></ul> Technical depth <ul><li>Set the architecture for AI and data products spanning forecasting, optimization, simulation, GenAI and agentic workflows, and defend it in technical review.</li><li>Work in the codebase. Review designs and pull requests, prototype where it resolves ambiguity faster than discussion, and contribute directly when delivery depends on it.</li><li>Own production quality: model robustness, explainability, evaluation, monitoring, latency and cost in service, not only at launch.</li><li>Build on the platform. Consume governed data through the semantic layer and apply CoE guardrails rather than constructing parallel capability.</li></ul> Business and delivery leadership <ul><li>Translate operational problems into technical solutions and, equally, translate technical constraints into terms operations and commercial leadership can act on.</li><li>Own the business case with the business product owner: the benefit hypothesis, the measurement approach and the reported outcome.</li><li>Lead without direct authority across engineering, data science, operations and regional teams, including delivery through the capability center and vendor partners.</li><li>Work from the frontline.</li><li>Regular Gemba at stations, with operational understanding built firsthand.</li></ul> Requirements: <ul><li>10+ years in engineering, applied AI or data science, with a substantial portion spent building and running systems in production rather than specifying them.</li><li>Demonstrable record of owning technical products end to end and scaling them beyond a first implementation into multi site or multi-market operation.</li><li>Depth in one or more of: forecasting (time series, probabilistic, hierarchical), optimization (MIP, LP, heuristics), simulation, or production GenAI and agentic systems, with working literacy across the others.</li><li>Strong Python and SQL, with practical command of the modern data and ML stack: cloud data platforms, pipelines, model deployment, evaluation and monitoring.</li><li>Experience influencing senior stakeholders and operating across engineering, operations and commercial functions without direct reporting lines.</li><li>Aviation, logistics or complex physical operations exposure is an advantage, not a prerequisite.</li></ul> Qualifications <ul><li>Bachelors, MSc or PhD in Computer Science, Engineering, Data Science, Mathematics, Operations Research or a related field. Personal characteristics</li><li>Technical credibility earned through building, not through managing builders.</li><li>Judgement on trade-offs between scope, speed and quality, with the confidence to decide under incomplete information.</li><li>Clear communication with engineers, operators and executives, adjusting register without losing precision.</li><li>Comfort with ambiguity and being accountable for outcomes rather than activity.</li></ul>

Responsibilities: Customer Experience Strategy &amp; Governance: <ul><li>Develop, implement and continuously evolve the Companys Customer Experience strategy, roadmap, service standards and priorities in alignment with overall business objectives.</li><li>Establish the Customer Experience governance framework and ensure clear accountability for customer experience performance across relevant functions.</li><li>Represent the Voice of the Customer within the organization and ensure customer insights are considered in strategic, commercial, product and operational decisions.</li><li>Establish cross-functional governance for significant customer-impacting issues, ensuring root causes, corrective actions, responsible owners and deadlines are defined and tracked through to closure.</li><li>Collaborate with Commercial, Marketing, Product, Technology, Network, Finance, Regulatory, Legal and other departments to deliver a consistent and seamless end-to-end customer experience.</li></ul>&nbsp;Customer Support Operations: <ul><li>Lead the Customer Support Operations function and ensure effective management of internal and outsourced customer support operations.</li><li>Ensure customers receive timely, consistent and high-quality support across all relevant customer service channels and touchpoints.</li><li>Define and monitor operational KPIs and SLAs, including service levels, response and resolution times, First Contact Resolution, productivity and other relevant customer support indicators.</li><li>Continuously optimize customer support processes to improve efficiency, reduce customer effort, improve service quality and control operating costs.</li><li>Oversee internal and outsourced inbound and outbound customer support operations and ensure third-party partners meet agreed service levels, productivity, quality and customer experience standards.</li><li>Define workforce dimensioning, capacity planning and resource allocation requirements for internal and outsourced operations based on customer demand, contact volumes and business requirements.</li><li>Establish and oversee effective escalation processes for complex, sensitive or unresolved customer issues, ensuring clear ownership and timely resolution.</li><li>Ensure appropriate business continuity and customer-support procedures are in place for network incidents, system outages, major campaigns, unexpected increases in customer demand and other high-impact events.</li><li>Assess and recommend technologies, systems, automation and self-service solutions that improve customer experience, operational efficiency and cost-to-serve.</li><li>Define the Customer Experience organizational structure and resource requirements, and oversee recruitment priorities, performance management, employee development and succession planning.</li></ul> Quality Assurance &amp; Continuous Improvement: <ul><li>Lead a structured Continuous Improvement program across customer-facing operations using customer feedback, operational data, quality findings and root-cause analysis.</li><li>Own the Customer Experience Quality Assurance framework, including quality standards, interaction audits, mystery evaluations, calibration, quality scorecards and corrective-action mechanisms across internal and outsourced operations.</li><li>Analyze customer complaints, repeat contacts, escalations, quality findings and operational data to identify recurring issues and root causes, and drive cross-functional corrective and preventive actions through to implementation.</li><li>Identify opportunities to reduce avoidable and repeat customer contacts through process simplification, automation, self-service, improved communication and permanent resolution of root causes.</li><li>Track Continuous Improvement initiatives and measure their impact on customer satisfaction, service quality, operational efficiency, contact reduction and other relevant business outcomes.</li><li>Lead end-to-end customer journey reviews and service-design initiatives to identify pain points, reduce customer effort, improve First Contact Resolution and enhance the experience across physical, assisted and digital touchpoints.</li></ul> Training, Knowledge &amp; Capability Development: <ul><li>Oversee the Customer Experience Training function and establish a structured capability-development framework covering onboarding, product and service knowledge, systems, processes, customer handling, soft skills, coaching and refresher training.</li><li>Ensure Training Needs Analysis is driven by quality results, customer feedback, operational performance and identified competency gaps, and measure the effectiveness of training interventions.</li><li>Establish and maintain effective Knowledge Management practices, including customer-care SOPs, scripts, FAQs, knowledge articles and operational guidelines, ensuring frontline teams receive accurate and timely information.</li><li>Promote a culture of customer focus, continuous learning and high performance through coaching, recognition and employee-development initiatives.</li></ul> Customer Analytics, Voice of Customer &amp; Insights: <ul><li>Lead the Customer Analytics function and ensure customer and operational data is converted into actionable insights for Customer Experience and the wider business.</li><li>Establish CX dashboards and reporting covering customer behaviour, contact drivers, complaints, repeat contacts, service performance, quality, satisfaction, retention/churn indicators and emerging customer trends.</li><li>Own the Voice of Customer and CX measurement framework, including NPS, CSAT, First Contact Resolution and other relevant customer experience indicators, establishing targets and monitoring performance against them.</li><li>Use surveys, complaints, customer interactions, research and other feedback channels to identify customer needs, expectations, pain points and emerging trends, ensuring insights are translated into clearly defined improvement actions.</li><li>Utilize customer segmentation based on relevant behaviors, preferences, value, demographics and usage patterns to support customer experience initiatives, service prioritization and relevant business campaigns.</li></ul> Product, Process &amp; Operational Readiness: <ul><li>Ensure Customer Experience is appropriately involved in the development and implementation of new products, services, campaigns, processes and customer-impacting system changes, with customer experience risks identified before implementation.</li><li>Establish operational-readiness requirements for major launches and changes, including capacity, processes, systems, training, knowledge materials, customer communication and escalation procedures, and monitor customer impact following implementation.</li></ul> Performance, Compliance &amp; Financial Management: <ul><li>Provide senior management with regular reporting on Customer Experience performance, customer trends, key risks, operational performance, Continuous Improvement initiatives and areas requiring management attention.</li><li>Ensure Customer Experience operations comply with applicable regulatory requirements, company policies, data protection requirements and industry standards in coordination with Regulatory, Legal and other relevant functions.</li><li>Develop and manage the Customer Experience operating budget and monitor operating costs, productivity and cost-to-serve, identifying opportunities to improve efficiency without negatively impacting customer experience.</li><li>Manage key Customer Experience vendors and partners, ensuring contractual commitments, SLAs, costs, quality standards and performance expectations are effectively monitored and managed.</li></ul>&nbsp;Requirements: <ul><li>Bachelor’s Degree in Business Administration, Communications, Marketing, or a related field,</li><li>A Master’s degree or professional certification (e.g., CCXP - Certified</li><li>Customer Experience Professional) is highly preferred.</li></ul>

Responsibilities: <ul><li>Lead the development and scaling of next-generation advanced packaging technologies, leveraging expertise in thin film deposition processes including Physical Vapor Deposition (PVD), Chemical Vapor Deposition (CVD), and/or Atomic Layer Deposition (ALD) to enable customer roadmaps and address critical technology challenges.</li><li>Drive process and technology roadmap development for advanced packaging, 3D integration, and related semiconductor applications through collaboration with customers, research organizations, and cross-functional engineering teams.</li><li>Manage engineers in utilizing techniques to characterize hardware, define methods and new technologies and apply them to characterize hardware, and/or perform hardware characterization on extremely complex systems for entire range of products for division</li><li>Interact with key customers to resolve extremely complex process engineering issues to address customers High Value Problems (HVP) for an entire range of products for the division.</li><li>Ensures effective product/technology demonstrations.</li><li>Leads collaboration efforts with executive managers and junior managers to ensure strategic execution.</li><li>Leads market share improvement efforts.</li></ul> Requirements: <ul><li>7-10+ years of semiconductor equipment R&amp;D experience.</li><li>2+ years of people management/team leadership experience.</li><li>Strong background in:&nbsp;</li><li>CVD, PVD, ALD, Etch</li><li>Thin-film technologies</li><li>Experience in equipment/process R&amp;D and product development.</li><li>Strong English communication skills.</li></ul>

Responsibilities: <ul><li>Develop, plan and execute Autonomous Maintenance, Preventive, Predictive Maintenance and TPM programme to reduce equipment failures and maintenance costs.</li><li>Lead, manage, and develop the maintenance team, including engineers, supervisors, technicians and contractors. Supervise, train, and mentor team in technical skills, safety practices, and lean methodologies.</li><li>Develop skill matrices for engineers, supervisors, and technicians, establish training and shift mechanisms, organize major-failure emergency response, and strengthen autonomous maintenance and cross-functional collaboration.</li><li>Lead root cause analysis and troubleshooting of recurring machine issues, implementing long-term strategy for improvement opportunities.</li><li>Work with external contractors and vendors for specialised maintenance work, major repairs and machines performance improvement</li><li>Develop data-driven maintenance plans and optimize spare parts inventory based on usage trends and failure patterns.Monitor and analyse maintenance-related data to identify trends and opportunities for optimisation.</li><li>Implement and manage machine maintenance and repair procedures to improve maintenance efficiency</li><li>Analyze machine performance data, focusing on Mean Time Between Failures (MTBF), Mean Time To Repair (MTTR), and Overall Equipment Effectiveness (OEE).</li><li>Ensure all maintenance activities comply with company safety standards and applicable regulations. Promote a strong safety culture and ensure compliance with permit-to-work and lockout/tagout procedures.</li><li>Prepare &amp; manage CAPEX, maintenance budgets and inventory control, including procurement of materials, spare parts stock levels and equipment</li><li>Maintain comprehensive maintenance records, equipment logs and documentation for compliance and future reference</li><li>Provide regular reports and performance dashboards to management, highlighting key insights and progress on improvement actions.</li><li>Any other duties as assigned by immediate superior from time to time.</li></ul> Requirements: <ul><li>Bachelor’s degree in engineering (Mechanical, Mechatronics, or related field).</li><li>At least 10 - 15 years of experience in maintenance management, with a proven track record in overseeing complex maintenance operations. &amp; 2-3 years of people management experience</li><li>Solid understanding of semiconductor and automated testing equipment’s, maintenance practices, and machine tool performance.</li><li>Strong leadership, communication, planning and troubleshooting capabilities.</li><li>Able to interpret PLC, engineering drawings, electrical diagrams and OEM technical documentation.</li><li>Must have hands-on Engineering and Maintenance experience in automation manufacturing</li></ul>

Responsibilities: <ul><li>Partner with development engineers, commodity teams, and suppliers to create innovative PCB solutions.</li><li>Contribute to the development of PCB commodity strategy.</li><li>Participate in the supplier selection process. Assess and qualify PCB suppliers. Take part in technical and process audits of suppliers as needed.</li><li>Lead PCB failure analysis and drive containment, corrective actions, and risk assessment.</li><li>Work closely with manufacturing and engineering teams to resolve complex technical issues.</li><li>Collaborate with cross-functional teams across the organization. Effectively communicate complex technical concepts and solutions.</li></ul> Requirements: <ul><li>Possess Engineering/Chemistry/Material degree or equivalent, plus 10+ years PCB manufacturing experience.</li><li>Solid understanding and direct experience in PCB design specifications and requirements, qualification, manufacturing fabrication and test, coupled with familiarity of materials (laminates and resins) and reliability knowledge and experience.</li><li>Competency in failure analysis techniques such as FMEA, 8D/Corrective Action, Fishbone IFD, Cause and Effect Analysis, Design of Experiments (DOE), and risk assessment.</li><li>Experienced in managing supplier relationships and implementing corrective actions and rigorous quality control measures to ensure product standards and reliability are consistently met.</li></ul>

Responsibilities: <ul><li>Lead and program-manage the development and release to manufacturing of innovative silicon photonics products.</li><li>Manage the complete product lifecycle from pre-concept commitment to First Customer Ship (FCS).</li><li>Collaborate with Optics Business Units (BUs), Product Lifecycle Management (PLM), and Engineering teams.</li><li>Lead cross-functional teams within Optics Operations to deliver optical transceiver modules.</li><li>Ensure products meet time-to-market requirements and scheduled launch milestones.</li><li>Drive achievement of product cost targets and cost optimization goals.</li><li>Monitor and improve manufacturing yields to meet quality and performance objectives.</li><li>Develop and execute volume ramp plans to support production scale-up and customer demand.</li></ul> Requirements: <ul><li>Bachelors degree and 5 plus years minimum experience in supply chain, manufacturing, or product development</li><li>Experience working on new product introductions with manufacturers, suppliers, and product development teams</li><li>Extraordinary program management skills</li><li>Deep experience in leading cross-functional teams to achieve results by identifying and aligning to shared goals, priorities, and dedications. Able to hold peers, partners, and yourself accountable.</li><li>Understanding of optical product design and development methodologies.</li><li>Understanding of silicon photonics technology.</li><li>Strong analytical skills and ability to lead teams to solve complex technical and business problems.</li><li>Ability to influence key partners, senior leaders, and external partners.</li></ul>

Responsibilities: <ul><li>Work with engineering team for new system/product/hardware qualification and tuning optimization in production.</li><li>Develop scripts and tools to automate routine test tasks, data reporting, and hardware diagnostic procedures.</li><li>Oversee the design, characterization, and maintenance of probe cards and associated load board hardware to ensure signal integrity.</li><li>Analyze large-scale test data using Python and data frame-based tools to identify yield limiters and implement process improvements.</li><li>Partner with Design and Product Engineering teams to perform root-cause analysis on test failures and silicon performance issues.</li></ul>&nbsp;Requirements: <ul><li>B.S. or M.S. in Electrical Engineering, Computer Engineering, or related field</li><li>Hands-on experience with ATE platforms (e.g., Advantest, Teradyne).</li><li>Advanced proficiency in Python/Pandas for complex data manipulation</li><li>At least 3 years’ working experience on wafer probing systems, good understanding of wafer-level testing methodologies</li><li>Excellent communication skills to effectively coordinate with global cross-functional teams.</li></ul>&nbsp;Preferred Qualifications: <ul><li>Good understanding of the PID control and Piezoelectric effect</li><li>Be familiar with JMP software to perform data analysis&nbsp;</li></ul>

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145 complex traps Jobs found

Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Technology Obsolescence - Task force - Data Specialist (VP1)

Lead, AI & Data Products (Technical)

Customer Experience Director

Manager, Process Engineer (Deposition)

Equipment Maintenance Manager

Senior PCB Component Engineer

Product Lifecycle Supply Chain Project Manager (Optic Product)

Test Development Engineer

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Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Technology Obsolescence - Task force - Data Specialist (VP1)

Lead, AI & Data Products (Technical)

Customer Experience Director

Manager, Process Engineer (Deposition)

Equipment Maintenance Manager

Senior PCB Component Engineer

Product Lifecycle Supply Chain Project Manager (Optic Product)

Test Development Engineer

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