AI Product Manager

Total Number of Openings

An AI Tech Lead is responsible for leading the technical design development and production deployment of AI/ML and Generative AI solutions. This role provides handson technical leadership across the endtoend AI lifecycleproblem framing data readiness model development evaluation deployment monitoring and continuous improvementwhile ensuring solutions are scalable secure and aligned with enterprise standards.

The AI Tech Lead works closely with Product Managers Data Scientists Data Engineers Architects DevOps/MLOps teams and business stakeholders to translate product requirements into robust AI system designs and deliver highquality AI capabilities. The role serves as a technical subject matter expert for delivery squads mentors engineers and data scientists and drives best practices across model engineering cloud architecture (Azure) and responsible AI.

This position requires strong expertise in Python ML engineering cloud-native architecture MLOps and increasingly LLM/GenAI patterns (prompting RAG embeddings safety evaluation) along with the ability to lead crossfunctional teams through ambiguity.

Key responsibilities:

Lead endtoend technical delivery of AI/ML and GenAI products from design through production and support.
Define solution architecture for AI systems including data pipelines feature/embedding stores model serving APIs and integration with enterprise applications.
Own engineering standards for model development testing CI/CD deployment observability and reliability (SLOs/SLAs).
Partner with Product and Business stakeholders to translate requirements into technical specs system designs and delivery plans.
Guide model selection and implementation (classical ML deep learning NLP LLMs) including evaluation strategy and performance tradeoffs.
Design and implement MLOps processes: model versioning reproducibility automated training model registry deployment strategies (blue/green canary) drift detection and retraining workflows.
Develop and review code for data processing training pipelines inference services prompt orchestration and evaluation harnesses.
Ensure responsible AI practices: data privacy security controls bias detection explainability content safety and auditability.
Implement GenAI patterns such as RAG embeddings vector search caching guardrails prompt management and hallucination mitigation.
Drive performance optimization: latency throughput cost (GPU/CPU) and scalability for realtime and batch inference.
Troubleshoot production issues and lead root cause analysis (RCA) postincident actions and reliability improvements.
Mentor engineers/data scientists conduct design/code reviews and support skill development across the team.
Collaborate with platform teams to leverage and improve shared AI foundations (data products reusable components accelerators).

Required Qualifications:

Experience:
610 years of overall experience with 35 years in AI/ML engineering or applied ML delivery
Proven experience leading teams/squads and owning production AI systems endtoend

Education:
bachelors degree in computer science Engineering Data Science or a related field

Technical Skills:
Strong proficiency in Python and ML frameworks (e.g. PyTorch TensorFlow scikitlearn).
Strong knowledge of ML engineering: feature engineering training pipelines evaluation inference optimization.
Experience with Azure (or equivalent cloud) services for AI delivery including compute storage networking and security.
Handson experience with containerization and orchestration (Docker Kubernetes) and API development (REST/gRPC).
Practical experience with MLOps tooling and practices (CI/CD model registry monitoring drift detection).
Strong understanding of data engineering fundamentals (ETL/ELT batch/stream SQL data quality lineage).
Experience with model monitoring/observability and production operations (logging metrics tracing).
Familiarity with GenAI/LLM solution design (RAG embeddings prompt engineering evaluation guardrails).
Knowledge of secure engineering practices secrets management IAM/RBAC and enterprise governance standards.

Engineering & Leadership Skills:
Strong system design and architecture skills for scalable AI platforms and services.
Excellent problemsolving ability debugging skills and attention to quality.
Ability to communicate technical concepts clearly to both technical and nontechnical stakeholders.
Experience collaborating across global teams and influencing without authority.

Preferred Qualifications:

Experience building LLMbased applications using Azure OpenAI/OpenAI or similar including RAG with vector databases.
Experience with Azure ML Databricks Synapse Data Lake AKS and cloud-native observability stacks.
Experience with Responsible AI frameworks and compliance requirements (privacy security audit model risk).
Knowledge of Domain workflows (e.g. oil & gas / energy / reliability / maintenance) and industrial data types.
Experience with event-driven or streaming architectures (Kafka/Event Hubs) for real-time ML.
Certifications such as Azure Data/AI certifications or Kubernetes certifications.
Exposure to performance profiling and optimization for GPU workloads and cost governance.
Experience with test automation for ML systems (data tests model tests prompt tests regression suites).

What we can offer you

Everything we do at Chevron is guided by our values and our commitment toThe Chevron Way.

Have the opportunity to take part in world-leading energy projects advance your professional development and expand your career within an inclusive and collaborative workplace.

• Join a workplace where innovation collaboration and safety are at the core of how we work.

• Work in thoughtfully designed environments that support focus well-being and innovation enhanced by digital tools that enable seamless collaboration.

• Grow through structured learning mentorship and opportunities to contribute to impactful projects that align with Chevrons values and business priorities.

• Accesscomprehensive benefitsthat support your health well-being and work-life balance.

• Engage with emerging technologies and digital solutions that are shaping the future of energy and enterprise.

How to apply

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