Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Cloud Engineering Manager

Technology Obsolescence - Task force - Data Specialist (VP1)

Specialist, Application Monitoring & Dashboard Analytics (VP2)

Lead, AI & Data Products (Technical)

Customer Engineer

Inside Sales Engineer

Supplier Development Process Engineer

Product Manager

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: <ul><li>Lead the engineering, operation, and continuous improvement of enterprise cloud platforms across Microsoft Azure (primary), AWS, and GCP, including landing zones, networking, platform services, and secure cloud foundations.</li><li>Build and maintain standardized delivery pathways, CI/CD pipelines, infrastructure-as-code (IaC) modules, GitOps practices, and environment automation to enable consistent, scalable, and compliant deployments.</li><li>Drive an automation-first engineering model, reducing manual intervention through policy-as-code, automated workflows, AI-assisted engineering, and agent-based execution of routine operational activities.</li><li>Develop and mature AIOps capabilities, including anomaly detection, intelligent alert correlation, automated remediation, and measurable reduction of operational toil.</li><li>Design and operate infrastructure supporting enterprise AI workloads, including GPU/accelerated compute, managed AI services, model serving and inference endpoints, vector and retrieval services, and secure AI landing zones.</li><li>Partner with Data &amp; AI teams to provide scalable, secure infrastructure for AI solutions while managing capacity, quotas, workload isolation, and AI infrastructure consumption and cost reporting.</li><li>Lead cloud reliability, observability, and resilience, establishing service level objectives (SLOs), error budgets, metrics, logging, tracing, recovery playbooks, and validated RTO/RPO requirements.</li><li>Implement cloud security, identity, and compliance guardrails through secure baselines, policy-as-code, workload identities, privileged access controls, and Microsoft Entra ID integration.&nbsp;</li><li>Maintain platform compliance with applicable data protection and industry standards, including GDPR, CCPA, PCI-DSS, and SOC 2, in partnership with Global Cybersecurity and other governance teams.</li><li>Execute FinOps and cloud optimization initiatives, including tagging and cost allocation, rightsizing, scheduling, storage optimization, reservations, savings plans, committed spend, forecasting, and consumption reporting.</li><li>Support the organizations cloud-first transformation, including modernization and consolidation of VMware workloads and rationalization of AWS and GCP environments around strategic business requirements.</li><li>Lead and develop a globally distributed cloud engineering team, establishing engineering standards, structured code and design reviews, operational discipline, continuous learning, and accountability for cloud providers and engineering partners.</li></ul> Requirements: <ul><li>Bachelor’s degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent professional experience.</li><li>10+ years of progressive cloud and infrastructure engineering experience, including at least 3 years leading engineering teams with accountability for production platform services.</li><li>Expert-level, hands-on experience with Microsoft Azure, with demonstrated experience building and operating environments across AWS and GCP.</li><li>Deep experience with platform engineering and infrastructure-as-code, including Terraform, Bicep, GitOps, CI/CD pipelines, landing zones, and automated provisioning.</li><li>Demonstrated experience implementing cloud automation and AI-enabled operations, including AIOps, AI-assisted engineering, or agent-based automation.</li><li>Hands-on experience supporting AI/ML infrastructure, including GPU/accelerated compute, managed AI services, model serving, or AI workload platforms.</li><li>Strong production experience with Kubernetes (AKS, EKS, or GKE), cloud security, identity, observability, and reliability practices.</li><li>Experience leading distributed engineering teams and supporting highly available, business-critical technology environments.</li></ul> Preferred Skills <ul><li>Professional certifications such as Azure Solutions Architect Expert, AWS Certified Solutions Architect – Professional, Google Professional Cloud Architect, CKA, Terraform Associate, or FinOps Certified Practitioner.</li><li>Experience with Microsoft Entra ID, workload identity, privileged access, Conditional Access, zero-trust principles, and policy-as-code.</li><li>Experience with observability and SRE practices, including Grafana, Prometheus, Azure Monitor, CloudWatch, SLOs, and error budgets.</li><li>Working knowledge of FinOps, cloud cost optimization, tagging strategies, reservations, savings plans, and consumption reporting.</li><li>Experience migrating and modernizing VMware and on-premises workloads into cloud environments.</li><li>Experience leading global teams across the Americas, EMEA, and APAC.</li><li>Experience within aviation, transportation, logistics, field operations, or other 24/7/365 operational environments.</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>

Responsibilities: <ul><li>Gather and analyze user requirements for dashboard, monitoring, and reporting enhancements.</li><li>Design, develop, and maintain operational dashboards and monitoring solutions based on business and technical requirements.</li><li>Create, modify, and optimize queries to extract, analyze, and present data for business and operational reporting.</li><li>Develop reports and analytics that provide actionable insights into system performance, availability, and operational health.</li><li>Support and administer enterprise monitoring tools such as Splunk and Geneos.</li><li>Troubleshoot monitoring alerts, dashboard discrepancies, and reporting-related issues.</li><li>Participate in system implementation, enhancement, testing, and deployment activities.</li><li>Ensure compliance with organizational standards, governance, and audit requirements.</li><li>Prepare and maintain technical documentation, user guides, and operational procedures.</li><li>To identify area of monitoring coverage to be improved; and work with relevant teams to enable monitoring solution to improve monitoring coverage.</li><li>To identify area of triage efficiency to be improved; and work with relevant teams to implement solution to improve triage efficiency.</li><li>To identify area of automation of service recovery ability to be improved; and work with relevant teams to implement solution to automate service recovery ability.</li><li>To work with various stakeholders (i.e., service automation, level 2 (group) and level 3 production support etc) to implement application monitoring, service end-to-end dashboard, and automation of service recovery.</li></ul> Requirements: <ul><li>Strong hunter mentality with a passion for creating new business opportunities and opening new partner relationships.</li><li>Experience in business development, channel sales, partner sales, technology sales, or customer acquisition roles.</li><li>Comfort with outbound prospecting, cold outreach, networking, pipeline generation, and opportunity development.</li><li>Understanding of cloud technologies, managed services, software licensing, cybersecurity, or broader IT solutions.</li><li>Strong commercial acumen with the ability to influence stakeholders and build strategic business relationships.</li><li>Self-motivated, resilient, and driven by achieving growth targets, partner acquisition goals, and business outcomes.</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: <ul><li>Install, maintain, and upgrade customer equipment&nbsp;</li><li>Apply basic diagnostic techniques to assess equipment and address technical issues, with assistance from Senior Field Service Engineers [Customer Engineers]&nbsp;</li><li>Collaborate with Senior Field Service Engineers [Customer Engineers] and/or the customer to understand roadmaps, process flows, inflection points, requirements, and&nbsp;</li><li>business challenges&nbsp;&nbsp;</li><li>Train and mentor junior Field Service Engineers [Customer Engineers]&nbsp;</li><li>Perform preventative and corrective maintenance on electrical, vacuum, mechanical, plasma, hydraulic, and gas systems&nbsp;</li><li>Coordinate and communicate directly with customers&nbsp;</li><li>Fulfill additional duties, as assigned.&nbsp;</li></ul> Requirements: <ul><li>Completion of a Diploma and/or Bachelor’s degree, military technical training, field service experience, or trade certification&nbsp;</li><li>4-7 years of work experience in semiconductor industry or other equipment support industry&nbsp;</li><li>Advanced mechanical aptitude with pneumatics, hydraulics, electronics, vacuum, and/or thermodynamics&nbsp;</li><li>Strong knowledge of hand tools and their appropriate usage, including digital multimeters&nbsp;</li><li>Effective written and verbal communication skills&nbsp;</li><li>Basic knowledge of Microsoft Excel, Word, and PowerPoint&nbsp;&nbsp;</li><li>Possession of a valid driver’s license and the ability to obtain a passport, if travel is required.&nbsp;</li></ul>

Responsibilities: <ul><li>Identify and pursue new sales opportunities across PH market.&nbsp;</li><li>Build and maintain strong relationships with existing and potential customers, adapting communication styles to regional preferences.</li><li>Support external sales engineers by generating leads and qualifying prospects.</li><li>Generate quotation and revert to customers for standard products, tubing, hoses and parts.</li><li>Follow up with customer quotations.</li><li>Take orders for repeat business and ensure timely follow-up with clients.</li><li>Collaborate with cross-functional teams to align sales strategies with regional market trends.</li><li>Maintain accurate records of customer interactions and sales activities in CRM systems.</li></ul> Requirements: <ul><li>Good English communication skills.&nbsp;</li><li>2 to 5 years of experience in inside sales (cold calling / lead generation).</li><li>Background in maintaining accurate records of customer interactions and sales activities.</li><li>Cold calling, lead generation.</li></ul>

Responsibilities: Cross function collaboration: <ul><li>Coordinate Procurement, SQE, PDE, MPDE, Engineering Assess and document manufacturing capability, process maturity, and QMS compliance during PCBA new supplier qualification/Development.</li><li>Engage upstream and downstream raw material partners to improve delivery capability, capacity alignment, and supplier responsiveness.</li><li>Organize and lead regular cross-functional reviews with PDE, MPDE, and suppliers to address delivery risks, manufacturing readiness, and improvement actions.</li><li>Track and drive closure of cross-functional and supplier action items, escalating gaps as needed.</li></ul> NPI stage: <ul><li>Provide early manufacturing and engineer support for PCBA commodities from a sourcing and supply chain perspective to support supplier selection decisions (critical).</li><li>Conflict Management: Coordinate and facilitate DFM (Design for Manufacturing) discussions with suppliers, and Engineering to ensure manufacturing requirements are clearly understood and accepted (no design decision ownership).</li><li>Assess and document manufacturing capability, process maturity, and QMS compliance during PCBA new supplier qualification.</li><li>Act as a coordination interface between Procurement and Engineering, ensuring alignment on quality, commercial, and technical expectations without assuming design ownership.</li></ul> Approved Supplier Management: <ul><li>Drive yield, process stability, and delivery performance improvements at supplier sites.</li><li>Collaborate with suppliers and key supply chain nodes to improve lead time and delivery reliability, supporting company sample and production needs.</li><li>Partner with CM and Procurement to support SRM programs for strategic PCBA suppliers, with close tracking of action plan execution.</li><li>Facilitate cross-functional supplier improvement workshops (e.g. CoPQ reduction, lead time optimization) as a neutral coordinator, ensuring clear ownership and accountability.</li><li>Monitor progress and escalate unresolved issues where action items are not closed as committed.</li></ul> Requirements: <ul><li>Bachelor’s degree in Electrical / Automation/ Mechanical or any Engineering related majors.</li><li>At least 7 years of MPDE experience within PCBA (Industry relevant), connector and cable assembly Quality Management portion. Procurement, supply chain experience OR Total years of experience 10+ years in various industries including PCBA.</li><li>Knowledge on specific product and process as required (Connectors, Cable and Cable Assemblies, PCBA Tolerances and Cost Drivers)&nbsp;</li><li>In-depth knowledge of electronic manufacturing processes such as PCBA assembly (SMT/THT), box build, testing, and packaging</li><li>Understanding of supplier capabilities, capacity planning, and EMS operating models</li><li>Familiarity with DFM/DFA (Design for Assembly), component sourcing, and BOM management</li><li>Awareness of quality systems, certifications (ISO 9001,IATF 16949 and IPC- Association Connecting Electronics Industries), and related compliance standards</li><li>Ability to work with EMS partners on cost, yield, lead time, and continuous improvement</li><li>Experience supporting NPI, supplier qualification, and scale-up to mass production</li><li>Expert level with the tools related to supplier APQP process, include but not limited to PPAP, FMEA, CP, MSA, SPC, R@R and 8D methodology is a Plus.</li><li>Thorough knowledge of Problem-Solving tools (5-Why, Ishikawa/Fishbone diagrams, Fault Tree</li><li>Analysis)</li></ul>

Responsibilities : <ul><li>Engage executive and corporate users to understand needs, decision workflows and pain points.</li><li>Translate requirements into clear product definitions, user stories and acceptance criteria.</li><li>Run structured discovery and experimentation to validate value and feasibility before build commitments are made.</li><li>Identify opportunities to extend assigned products from task-level support to broader workflow automation, and contribute these to the product roadmap.&nbsp;</li><li>Own the end-to-end delivery of assigned AI products, including Rocket, from definition through to launch.</li><li>Ensure quality, usability and readiness criteria are met before release to a senior user base.&nbsp;</li><li>Own assigned products after launch, driving iterative improvement based on usage data and user feedback.</li><li>Define, track and report product KPIs covering adoption, usage, accuracy, satisfaction and time saved.</li><li>Register and maintain AI use cases for assigned products within the agreed portfolio management process.&nbsp;</li><li>Act as the day-to-day product interface for executive and corporate stakeholders.&nbsp;</li></ul> Requirements: <ul><li>3 – 6 years of experience in digital or AI product management, product ownership or equivalent delivery roles.&nbsp;</li><li>Bachelor’s degree in a quantitative field such as information technology, business, finance, engineering or mathematics.&nbsp;</li><li>Hands-on experience in product management across discovery, delivery and lifecycle.&nbsp;</li><li>Exposure to AI, automation or data-driven products.&nbsp;</li></ul>

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173 it cloud engineer Jobs found

Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Cloud Engineering Manager

Technology Obsolescence - Task force - Data Specialist (VP1)

Specialist, Application Monitoring & Dashboard Analytics (VP2)

Lead, AI & Data Products (Technical)

Customer Engineer

Inside Sales Engineer

Supplier Development Process Engineer

Product Manager

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Forward Deployed Senior Engineer, AI & Analytics

Senior AI Engineer

Cloud Engineering Manager

Technology Obsolescence - Task force - Data Specialist (VP1)

Specialist, Application Monitoring & Dashboard Analytics (VP2)

Lead, AI & Data Products (Technical)

Customer Engineer

Inside Sales Engineer

Supplier Development Process Engineer

Product Manager

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