Enterprise Analytics – Machine Learning Ops/AI Projects (VP1)

Forward Deployed AI Strategist

Data & ML Architect

Sr. Software Engineer - Ag & Trading .Net

Senior Machine Learning Engineer

IAD / SAD / LAD GCP Data Engineer

IAD / SAD Developer Ansible OpenShift

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Senior DevOps Engineer

Responsibilities: <ul><li>Develop and maintain automation scripts using Linux shell scripting, Python, or other relevant tools.</li><li>Develop and maintain the entire DevOps pipelines.</li><li>Ensure seamless deployment and integration between cloud/prem environments (AWS).</li><li>Integrate AI models into production environments using containerized platforms such as OpenShift.</li><li>Implement and maintain network security protocols to safeguard AI systems and data pipelines.</li><li>Collaborate with cross-functional teams to understand AI workflows and translate them into robust engineering solutions.</li><li>Monitor and optimize system performance, reliability, and scalability.</li><li>Support CI/CD processes and infrastructure for AI model deployment and updates.</li></ul> Requirements: <ul><li>Bachelor’s degree in Computer Science, Engineering, or a related field.</li><li>Bash and Unix/Linux command-line toolkit is a must-have.</li><li>Deep technical background, with hands-on experience in supporting CI/CD pipelines (Bitbucket, Jenkins, Artifactory, Sonar, Veracode for SAST, JIRA workflow, and Confluence).</li><li>Hands-on experience with OpenShift, Docker, Kubernetes.</li><li>Knowledge of cloud platforms (e.g. AWS) is a must-have.</li><li>Exposure to data and network security and compliance in AI systems.</li><li>Knowledge of API integration and microservices architecture.</li><li>Proficiency in Python used both for automation and ML-related tasks</li><li>Knowledge of Workflow Orchestrator, such as Ctrl-M</li><li>Good knowledge of Logging and Monitoring tools, such as Splunk and Geneos.</li><li>Experience with Observability framework, such as Langfuse, Elastic Stack, Grafana, Open Telemetry.</li><li>Understanding of Generative AI (e.g. prompt engineering, RAG pipelines) and Agentic AI concepts.</li></ul>

Role Description: <ul><li>The Strategy team is looking for a hands-on AI-enabled strategy and market intelligence professional to materially accelerate the way we conduct deep research, market intelligence, analytical modeling, and GTM analysis by applying high impact AI workflows and translating them into sharper strategic decisions.</li><li>The individual will partner with business, product, commercial, and tech teams to translate ambiguous questions into actionable insights, while developing and scaling AI-enabled solutions that improve strategic decision-making and drive measurable business impact.&nbsp;</li><li>The individual is expected to work in close partnership with our client IT team, to leverage existing AI &amp; data capabilities on Databricks &amp; AWS (where applicable) while developing solutions.</li></ul> Responsibilities: Strategic problem solving and driving cross-functional execution&nbsp; <ul><li>Translate AI-assisted analyses into practical executive decisions by applying strong business judgment, rigorous validation protocols, and a sharp “so-what” orientation</li><li>Collaborate with and influence senior stakeholders across strategy, sales, technology teams, and P&amp;L owners</li><li>Good first-principles and problem-solving capabilities with the ability to build and test strategic hypotheses Identifying meaningful opportunities to leverage AI and deploying scaled AI solutions at pace</li></ul> Identify, assess, and prioritize high-value AI and automation opportunities by translating business challenges into scalable, practical solutions&nbsp; <ul><li>Design proprietary AI-enabled workflows, agents, and accelerators for recurring strategy activities (competitor monitoring, earnings call tracking, tariff and regulatory intelligence, voice-of-customer mining etc.)</li><li>Build reusable templates, workflows, and modules to drive adoption and embed AI capabilities into strategy processes</li></ul> Executing rapid AI-augmented research and competitive intelligence&nbsp; <ul><li>Lead AI-augmented research using tools like Claude, OpenAI, Perplexity, AlphaSense, CapIQ etc. to deliver source-triangulated competitive and industry insights</li><li>Synthesize structured datasets (S&amp;P Capital IQ, Bloomberg, IDC, Gartner) and unstructured sources (10-Ks, analyst reports, expert interviews) – to generate and validate AI-assisted outputs</li></ul> Support strategy team in day-to-day execution by leveraging AI-led tools <ul><li>Lead AI-enabled market sizing, opportunity mapping, and TAM/SAM/PAM modelling</li><li>Accelerate financial modelling – scenario and sensitivity analyses, valuation, NPV/IRR, segment P&amp;Ls, pricing</li><li>Support M&amp;A pipeline by AI-screening targets and generating investment theses</li></ul> AI enablement, coaching and continuous innovation <ul><li>Coach strategy team members on prompting techniques, research workflows, and AI-fluent execution</li><li>Scale AI adoption across Corporate Strategy through hands-on mentorship of beginner users</li><li>Stay current on agentic stacks, multimodal models, and deep research agents</li></ul> Requirements: Skillset and capabilities AI fluency and hands-on deployment orientation&nbsp; <ul><li>Working knowledge of GenAI patterns – LLM capabilities/limitations, prompt design, etc.</li><li>Proven ability to convert business problems into reusable AI workflows, tools, or accelerators</li><li>Builder mindset with experience prototyping AI solutions using frontier tools &amp; low-code platforms</li></ul> Analytics, modelling, and research excellence <ul><li>Strong modelling skills across TAM/SAM/PAM, scenarios, sensitivities, NPV/IRR, valuation etc.</li><li>Ability to synthesize structured datasets with unstructured sources</li><li>Research discipline – source triangulation and rigorous validation of AI outputs</li></ul> Strategic problem solving, communication, executive presence, and influence&nbsp; <ul><li>Sharp "so-what" orientation to translate AI outputs into practical executive decisions</li><li>Crisp written and verbal communication, with ability to frame board-ready narratives</li><li>Skill in explaining AI workflows and implications to both technical and non-technical audiences</li><li>Stakeholder management across strategy, commercial, data, and technology teams</li></ul> Ownership, and execution <ul><li>Independently own workstreams end-to-end – from framing through deployment</li><li>High bar for quality; never publishes unchecked AI outputs or unvalidated analysis</li></ul> Coaching and capability building&nbsp; <ul><li>Patience and ability to coach colleagues on prompting, research workflows, and source validation</li><li>Track record of building reusable playbooks – templates and training modules that scale adoption</li></ul> Passion, curiosity, and learning agility <ul><li>Deep passion for AI, tech, and industrial innovation, with curiosity to stay current on frontier tools</li><li>Entrepreneurial ownership mindset and drive to build a new AI-enabled strategy capability</li></ul> Qualifications:&nbsp; <ul><li>BE / B-Tech in Computer Science Engineering, Data Science or a related analytical field required; MBA, MS, M-Tech or equivalent advanced degree strongly preferred.</li><li>3–4+ years of experience in AI transformation, analytics, or AI-enabled business building. Prior Forward Deployed Engineer / Solutions Architect / Senior Data Scientist background preferred.</li><li>Experience in a top-tier strategy consulting firm, AI-native consulting team, analytics firm or corporate / product strategy team role strongly preferred.</li><li>Demonstrated experience building or deploying 0–1 AI solutions (not just experimenting), GenAI, analytics, automation, or workflow solutions in a business environment required.</li><li>Proficiency in Excel and PowerPoint required; Python or equivalent scripting capability strongly preferred. Experience with DevOps, CI/CD pipelines, and infra as code (e.g., Jenkins, Docker, Kubernetes) required.</li><li>Proficient in implementing AI platforms like AWS Bedrock, LangGraph, Crew AI, Glean etc.</li><li>Knowledge of enterprise data environments (esp. SAP, Databricks) or cloud platforms (esp. AWS) is a plus. Experience integrating AI systems with enterprise data platforms required.</li><li>Experience with LLM-powered solutions, RAG systems, agentic workflows, AI research tools, or AI-enabled knowledge manage</li></ul>

Responsibilities: Strategic Leadership: <ul><li>Set the vision and direction for data and AI architecture for project and client.</li><li>Influence enterprise data and AI strategies, driving adoption of best practices in data, ML, and AI architecture.</li><li>Mentor and develop junior architects and engineers, fostering a culture of technical excellence and innovation.</li></ul> Architectural Excellence: <ul><li>Architect and design enterprise-scale data platforms and ML/AI solutions for scalability, reliability, and performance.</li><li>Define and enforce data and ML architecture standards, patterns, and governance frameworks for data pipelines, ML workflows, and analytics platforms.</li><li>Lead solution design for integrating data from diverse sources (streaming, batch, cloud, on-premises) using Azure, AWS, and GCP services.</li></ul> AI &amp; ML Leadership: <ul><li>Architect advanced Data Science, ML and AI solutions, including NLP, RAG LLM and Agentic AI systems.</li><li>Oversee the implementation of MLOps frameworks, ensuring robust deployment, monitoring, and lifecycle management of ML models.</li><li>Evaluate and recommend emerging AI/ML technologies and platforms, driving innovation in intelligent data products.</li><li>Provide Ethical AI leadership to ensure intelligent solutions are implemented when required and in safe methods.</li></ul> Technical Leadership: <ul><li>Oversee data modelling (e.g. medallion architecture), data governance, and quality frameworks. Implementing technical reviews and driving high standards.</li><li>Collaborate with engineering and DevOps teams to establish CI/CD and MLOps architectures using Azure DevOps, Kubernetes, and Terraform.</li><li>Guide the selection and implementation of visualization and analytics tools (Power BI, Tableau, PowerApps).</li></ul> Stakeholder Engagement: <ul><li>Engage with senior stakeholders to translate complex business requirements into technical solutions and strategic roadmaps.</li><li>Ensure all solutions meet security, compliance, and performance standards, and align with enterprise architecture principles.</li></ul> Innovation &amp; Emerging Technologies: <ul><li>Apply modern data and AI architecture patterns (Cloud Native, Steaming/batch ingestion, Agentic AI Design) and leverage platforms such as AI/ML, Big Data, and Data Lakehouse.</li></ul> Requirements: <ul><li>Minimum 8 years’ experience client facing roles specializing in Data and Architecture roles.</li><li>Experience in both Agile and Waterfall project environments.</li><li>Strong stakeholder management and communication skills.</li><li>Security and compliance awareness.</li><li>Industry certifications (e.g., AWS Solution Architect, Azure Data Engineer, GCP Data Engineer).</li><li>Experience mentoring or managing technical teams.</li></ul>

Responsibilities: <ul><li>Demonstrate accountability by ensuring timely and high-quality project delivery.</li><li>Be result driven, consistently achieving measurable outcomes and meeting goals.</li><li>Act as a team player, collaborating effectively with cross-functional teams.</li><li>Exhibit a can-do mentality, tackling challenges with a proactive and positive approach.</li><li>Apply software engineering patterns and principles to design, develop, test, integrate, maintain, troubleshoot and document complex and varied software solutions and incorporate security practices in newly developed and maintained applications.</li><li>Participate in peer code review sessions to review code, coach peers and ensure code quality.</li><li>Take the lead in the assigned agile team to adopt agile philosophies, facilitate agile ceremonies and identify continuous improvement opportunities.</li><li>Incorporate the companys engineering and development best practices within the full software development lifecycle including coding standards, code reviews, source control management, building processes, testing and security principles, to deliver high quality code rapidly.</li><li>Collaborate to lead demonstration and continuous feedback sessions to improve development and help drive the long term vision.</li><li>Participate in the engineering community by maintaining and sharing relevant technical approaches and modern skills.</li><li>Independently handle complex issues with minimal supervision, while escalating only the most complex issues to appropriate staff.</li><li>Other duties as assigned</li></ul> Requirements: <ul><li>Bachelor’s degree in a related field or equivalent experience</li><li>Confirmed experience automating pipelines for continuous integration, testing, delivery and security.</li><li>Confirmed experience architecting applications, databases, services or integrations.</li><li>Extensive experience with .NET Framework and .NET Core.</li><li>Familiarity with front-end technologies like HTML, CSS, JavaScript, and frameworks such as Angular, React, or Vue.js.</li><li>Experience with DevOps practices and tools such as Docker and CI/CD pipelines.</li><li>Knowledge of SQL Server and / or PostgreSQL for database design and management.</li><li>Hands-on experience with Kubernetes for container orchestration and management.</li><li>Experience with cloud platforms such as Azure, AWS, or GCP.</li><li>Minimum of 5 years of related work experience in software development using .NET technologies.</li></ul>

Role Description: <ul><li>Working closely with the Data Scientists and be responsible for implementing large language models (LLMs) based applications, working with proprietary and open-source Models as well as popular frameworks such as Lang Chain to ensure seamless integration and deployment.</li></ul> Responsibilities: <ul><li>Building standalone applications that interact with LLM models.</li><li>Building RAG-based applications.</li><li>Understanding of Vector DBs like Solr for LLM based applications</li><li>Integrate models with existing systems and APIs.</li><li>Implement production quality ETL jobs</li><li>Preprocess and manage data for training and deployment.</li><li>Collaborate with cross-functional teams to define, design, and ship new features.</li><li>Write clean, maintainable, and efficient code.</li><li>Document development processes, code, and APIs.</li></ul> Requirements: <ul><li>Degree in Computer Science, Data Science, or related field.• Proven experience in building customer facing ML based APIs</li><li>Mexico City–based (or nearby), available to work onsite 2 days per week at Glorietadel Ahuehuete, Reforma.</li><li>Experience in developing applications that are scalable to handle TBs of data</li><li>Strong knowledge of API integration (RESTful, GraphQL).</li><li>Experience with data preprocessing, SQL, and NoSQL databases as well as vector stores (e.g., Postgres, MySQL, Solr, Elasticsearch/OpenSearch, etc.).</li><li>Familiarity with deployment tools (Docker, Kubernetes).</li><li>Experience with DevOps tools like Jenkins, Terraform, Cloud Formation templates is highly preferred.</li><li>Excellent problem-solving and communication skills.</li><li>Experience with Spark/Hadoop, EMR or any other Big Data technology would be a plus</li><li>Experience working on agents is considered a good-to-have</li><li>Familiarity with agents, even through courses Work in a way that works for you We promote a healthy work/life balance across the organization..</li></ul> Other Information: These are some of the benefits we are delighted to offer: <ul><li>Private Medical/Dental Plan.</li><li>Savings Fund.</li><li>Life Insurance.</li><li>Meal/Grocery Voucher.</li><li>We offer an appealing working prospect for our people.</li><li>With numerous wellbeing initiatives, shared parental leave, study assistance and sabbaticals, we will help you meet your immediate responsibilities and your long-term goals.</li><li>Working flexible hours - flexing the times when you work in the day to help you fit everything in and work when you are the most productive Working for you.</li><li>We know that your well-being and happiness are key to a long and successful career.</li></ul>

Responsibilities: Architecture &amp; Design:&nbsp; <ul><li>Design end-to-end data architectures that integrate real-time streaming (Kafka, GCP Pub/sub) and batch processing (Big Query OR Databricks).</li></ul> Pipeline Development:&nbsp; <ul><li>Build and maintain robust ETL/ELT pipelines flowing into BigQuery/Databricks, ensuring data is clean, modeled, and ready for consumption.</li></ul> Custom Integration:&nbsp; <ul><li>Develop custom APIs and microservices using Java/Python/C# to facilitate data movement between niche systems and the data lakehouse.</li></ul> Infrastructure Management:&nbsp; <ul><li>Deploy and manage data workloads on Google Kubernetes Engine (GKE), ensuring high availability and cost-efficiency.</li></ul> Technical Leadership:&nbsp; <ul><li>Mentor junior engineers, conduct code reviews, and establish DataOps best practices (CI/CD, automated testing, and monitoring).</li></ul> Data Reliability:&nbsp; <ul><li>Implement data quality frameworks to ensure accuracy and lineage across the hybrid environment.</li></ul> Requirements: <ul><li>Experience with Terraform &amp; Azure Dev Ops (ADO).</li><li>Knowledge of workflow orchestrators like Apache Airflow (Cloud Composer).</li><li>Experience implementing Data Governance (metadata management, data cataloging).</li><li>Data Processing &amp; Storage</li><li>BigQuery: Expert-level SQL and experience with partition/cluster optimization.</li><li>Databricks: Deep knowledge of the Lakehouse architecture, Delta Lake, and Spark (PySpark/Scala) Software Engineering &amp; APIs</li><li>Languages: Proficiency in Java/Python/C# for backend development and custom data connectors.</li><li>API Design: Proven ability to build and consume RESTful APIs for data ingestion Streaming &amp; Infrastructure</li><li>Kafka/GCP Pub-Sub: Experience building event-driven architectures and managing producers/consumers.</li><li>GKE: Proficiency in containerization (Docker) and orchestrating data workloads on Kubernetes.</li></ul>

Responsibilities: <ul><li>Perform systems analysis and design for applications deployed using Ansible and OpenShift.</li><li>Design and develop moderate to complex applications in alignment with business and technical requirements.</li><li>Create, maintain, and ensure accuracy of comprehensive application documentation.</li><li>Define, build, and deliver application and system integration builds.</li><li>Monitor, evaluate, and apply emerging technology trends to improve system architecture and development practices.</li></ul> Requirements: <ul><li>5+ years of experience</li><li>Bachelor’s Degree or International equivalent&nbsp;</li><li>Extensive experience with core Java concepts and Java EE.</li><li>Extensive experience with Spring and Spring Boot.</li><li>Experience with CI/CD code automation, Ansible &amp; DevOps</li><li>Experience with OpenShift</li><li>Good understanding of software architecture and design principles.</li><li>Understanding of data structures, programming logic, and design</li><li>Understanding of application design patterns</li><li>Excellent written &amp; verbal communication skills</li><li>Excellent attention to detail</li></ul>

Responsibilities: <ul><li>Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.&nbsp;</li><li>Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.&nbsp;</li><li>Implement automated retraining, drift detection, and re-deployment of ML models.&nbsp;</li><li>Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.&nbsp;</li><li>Implement model monitoring, observability, and alerting across accuracy, latency, and cost.&nbsp;</li><li>Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.&nbsp;</li><li>Ensure pipelines are secure, compliant, and cost-optimized.&nbsp;&nbsp;</li><li>Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance .&nbsp;</li><li>Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.&nbsp;</li><li>Stay current with the latest ML research and apply new tools pragmatically to production systems.&nbsp;</li><li>Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.&nbsp;</li></ul> Requirements: <ul><li>Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).&nbsp;</li><li>5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.&nbsp;</li><li>Strong foundations in data structures, algorithms, and distributed systems.&nbsp;</li><li>Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.&nbsp;</li><li>Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).&nbsp;</li><li>Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).&nbsp;</li><li>Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.&nbsp;</li><li>Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).&nbsp;</li><li>Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)&nbsp;</li><li>Strong understanding of statistical methods, ML algorithms, and deep learning architectures.&nbsp;</li></ul>

Responsibilities: <ul><li>Provide application L1/L2 support and system checks (CPU, disk usage, logs, services)</li><li>Troubleshoot and resolve system and application issues, and provide on-site technical support to ensure timely resolution and minimal disruption to business operations</li><li>Monitor application health and system alerts, escalating issues as needed</li><li>Log incidents and service requests in the ticketing system, ensuring timely resolution</li><li>Coordinate with L3 teams and vendors for issue escalation and follow-up</li><li>Perform routine maintenance tasks, such as application updates, patching, and backups</li><li>Maintain clear documentation of issues, fixes, and procedures</li><li>Participate in system testing, deployment, and post-deployment support activities</li><li>Assist in user access management and configuration </li><li>Requirements:</li><li>Diploma/Degree in Electrical Engineering, Computer Science, Communications Engineering or equivalent</li><li>Minimum 2 years in application maintenance, support or server infrastructure setup</li><li>Experience participating in Kubernetes cluster projects, with proficiency in using relevant commands to check application logs</li><li>Experience with at least two of the following Virtualization Technology/Platforms: VMware, Xen, KVM, OpenStack, Cloud Stack, Azure Stack</li><li>Experience with cloud orchestration, configuration management and auto-scaling techniques</li><li>Thorough knowledge of networking, routers, switches, and WiFi setup</li><li>Hands on experience in deploying ceph using different file system such as object storage, rbd, cephfs storage</li><li>Good knowledge of storage technologies, such as NAS, SAN, SDS</li><li>Experience with automation tools such as Salt/Chef/Puppet/Ansible</li><li>Experience with load balancing setup for TCP/IP, HTTP, DNS, routings.</li><li>Experience in using software version control</li><li>Knowledge of IT security standards, for instance from CIS or IM8.</li><li>Experience in IT security policy management and enforcing IT security</li><li>Knowledge of computer hardware components and ability to restore faulty servers to working condition.</li><li>Experience in coding or scripting languages such as bash, windows batch script, powershell, perl, python, etc.&nbsp;</li></ul>

Responsibilities: Cloud Infrastructure Design and Deployment: <ul><li>Architect, design, and implement scalable, reliable, and secure solutions on GCP.</li><li>Deploy and manage GCP services in both development and production environments, ensuring seamless integration with existing infrastructure.</li><li>Implement and manage core services such as BigQuery, Datafusion, Cloud Composer (Airflow), Cloud Storage, Data Fusion, Compute Engine, App Engine, Cloud Functions and more.</li></ul> Infrastructure as Code (IaC) and Automation: <ul><li>Develop and maintain infrastructure as code using Terraform or CLI scripts to automate provisioning and configuration of GCP resources.</li><li>Establish and document best practices for IaC to ensure consistent and efficient deployments across environments.</li></ul> DevOps and CI/CD Pipeline Development: <ul><li>Create and manage DevOps pipelines for automated build, test, and release management, integrating with tools such as Jenkins, GitLab CI/CD, or equivalent.</li><li>Work with development and operations teams to optimize deployment workflows, manage application dependencies, and improve delivery speed.</li></ul> Security and IAM Management: <ul><li>Handle user and service account management in Google Cloud IAM.</li><li>Set up and manage Secrets Manager and Cloud Key Management for secure storage of credentials and sensitive information.</li><li>Implement network and data security best practices to ensure compliance and security of cloud resources.</li></ul> Performance Monitoring and Optimization: <ul><li>Monitoring &amp; Security: Set up observability tools like Prometheus, Grafana, and integrate security tools (e.g., SonarQube, Trivy).</li><li>Networking &amp; Storage: Configure DNS, networking, and persistent storage solutions in Kubernetes.</li><li>Set up monitoring and logging (e.g., Cloud Monitoring, Cloud Logging, Error Reporting) to ensure systems perform optimally.</li><li>Troubleshoot and resolve issues related to cloud services and infrastructure as they arise.</li></ul> Workflow Orchestration: <ul><li>Orchestrate complex workflows using Argo Workflow Engine.</li><li>Containerization: Work extensively with Docker for containerization and image management.</li><li>Optimization: Troubleshoot and optimize containerized applications for performance and security.</li></ul> Requirements:﻿ <ul><li>Bachelors or masters degree in computer science, Information Technology, or a similar field</li><li>Must have 10 + years of extensive experience in designing, implementing, and maintaining applications on GCP and OpenShift</li><li>Comprehensive expertise in GCP services such as GKE, Cloudrun, Functions, Cloud SQL, Firestore, Firebase, Apigee, GCP App Engine, Gemini Code Assist, Vertex AI, Spanner, Memorystore, Service Mesh, and Cloud Monitoring</li><li>Solid understanding of cloud security best practices and experience in implementing security controls in GCP</li><li>Thorough understanding of cloud architecture principles and best practices</li><li>Experience with automation and configuration management tools like Terraform and a sound understanding of DevOps principles</li><li>Proven leadership skills and the ability to mentor and guide a technical team</li></ul>

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12 kubernetes Jobs found

Enterprise Analytics – Machine Learning Ops/AI Projects (VP1)

Forward Deployed AI Strategist

Data & ML Architect

Sr. Software Engineer - Ag & Trading .Net

Senior Machine Learning Engineer

IAD / SAD / LAD GCP Data Engineer

IAD / SAD Developer Ansible OpenShift

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Senior DevOps Engineer

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12 kubernetes Jobs found

Enterprise Analytics – Machine Learning Ops/AI Projects (VP1)

Forward Deployed AI Strategist

Data & ML Architect

Sr. Software Engineer - Ag & Trading .Net

Senior Machine Learning Engineer

IAD / SAD / LAD GCP Data Engineer

IAD / SAD Developer Ansible OpenShift

Senior Machine Learning Engineer (Production & MLOps Focus)

System engineer

Senior DevOps Engineer

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