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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 hands‑on technical leadership across the end‑to‑end AI lifecycle—problem framing, data readiness, model development, evaluation, deployment, monitoring, and continuous improvement—while 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 high‑quality 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 cross‑functional teams through ambiguity.

Key responsibilities:

• Lead end‑to‑end 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 trade‑offs.
• 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 real‑time and batch inference.
• Troubleshoot production issues and lead root cause analysis (RCA), post‑incident 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:
• 6–10+ years of overall experience with 3–5+ years in AI/ML engineering or applied ML delivery
• Proven experience leading teams/squads and owning production AI systems end‑to‑end

Education:
• bachelor’s degree in computer science, Engineering, Data Science, or a related field

Technical Skills:
• Strong proficiency in Python and ML frameworks (e.g., PyTorch, TensorFlow, scikit‑learn).
• 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.
• Hands‑on 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 problem‑solving ability, debugging skills, and attention to quality.
• Ability to communicate technical concepts clearly to both technical and non‑technical stakeholders.
• Experience collaborating across global teams and influencing without authority.

Preferred Qualifications:

• Experience building LLM‑based 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 to The 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 Chevron’s 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

To be a part of our success, click APPLY to submit your application.

Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time-limited Visa status for this position. 

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“ENGINE” refers to the Chevron Engineering and Innovation Excellence Center, which operates under Chevron Global Technology and Services Private Limited, an affiliate of Chevron Corporation. References to ENGINE in this document are for convenience only and do not denote a separate legal entity.

This job advertisement is intended to provide a general overview of the role and workplace environment. Role details and outcomes may vary based on business needs and individual performance.

Chevron participates in E-Verify in certain locations as required by law.

Chevron ENGINE supports global operations, supporting business requirements across the world. Accordingly, the work hours for employees will be aligned to support business requirements. The standard work week will be Monday to Friday. Working hours are 8:00am to 5:00pm or 1.30pm to 10.30pm.

Privacy Terms and Conditions

We respect the privacy of candidates for employment. This Privacy Notice sets forth how we will use the information we obtain when you apply for a position through this career site. If you do not consent to the terms of this Privacy Notice, please do not submit information to us.

Please access the Global Application Statements, select the country where you are applying for employment. By applying, you acknowledge that you have read and agree to the country specific statement.

Terms of Use