Associate AI Engineer, OI&A

The Associate AI Engineer role within Amgen’s OI&A organization focuses on delivering end-to-end data science, software engineering, and GenAI solutions for enterprise functions. The work spans AI/ML and GenAI engineering, architecture and integration, evaluation and governance, and production operations in hybrid environments.

• Location: Thousand Oaks, CA
• Work model: Hybrid

• Salary range: USD 81, per year

In this position, you will lead integrated end-to-end solution engineering across AI/ML, RAG and agents, MLOps and LLMOps, evaluation and governance, security, and production operations. The role emphasizes moving from business workflow discovery to executable delivery plans and measurable adoption and value.

• Lead discovery by clarifying the business workflow, users, intended outcome, value hypothesis, acceptance criteria, operational constraints, data readiness, integration dependencies, and production implications.
• Translate complex problems into an executable solution design, delivery plan, technical work streams, estimates, milestones, dependencies, risks, acceptance criteria, release approach, and support transition.
• Build, prototype, review, or contribute to critical production components to prove feasibility or unblock delivery, including AI-enabled applications, RAG, bounded agents, intelligent automation, APIs, and integrations.
• Define and maintain integrated architecture across applications, workflows, data and knowledge pipelines, models, retrieval, agents, APIs, enterprise integrations, identity and access controls, observability, and human review.
• Orchestrate delivery across full-stack software and AI engineering, data science, ML and context engineering, testing, platform, security, compliance, and business roles.
• Establish integrated testing, AI evaluation, and governance covering functional, performance, security, data, model, retrieval, generation, tool-use, human oversight, and operational behavior with explicit release thresholds.
• Coordinate production readiness through CI/CD, staged release, monitoring, logging, SLOs, rollback, recovery, runbooks, and controlled deployment, including early issue triage, stabilization, and transition to the operating owner.
• Communicate evidence, risks, trade-offs, and status clearly; measure adoption and value; and convert delivery lessons into reusable components, accelerators, standards, documentation, and playbooks.

• Education and experience: Bachelor’s degree in Computer Science, Engineering, Data Science or related field OR Diploma with 2+ years of experience in Computer Science, Engineering, Data Science, or related field
• Enterprise solution architecture and integration: End-to-end design across applications, APIs, services, data and knowledge flows, models, retrieval, agents, workflows, persistence, identity, security zones, enterprise systems, and support boundaries.
• Applied AI/ML and GenAI engineering: Production Python and SQL; awareness of classical ML and NLP; foundation-model integration; prompt and context management; RAG; structured output; provenance and citations; bounded tool use; permissions; recovery; and human control.
• Cloud, Dev Sec Ops  and lifecycle operations: Cloud-native services; containers; CI/CD; infrastructure as code; versioning; observability; SLOs; staged release; rollback; incidents; disaster recovery; capacity;
  Fin Ops; runbooks; and MLOps/LLMOps.
• Hands-on proficiency: Strong hands-on capability in Python and SQL, including designing or reviewing production software, APIs, services, data flows, evaluation pipelines, and enterprise integrations.
• Delivery and coordination: Ability to turn complex business problems into coherent technical designs, executable delivery plans, acceptance criteria, and production-readiness evidence while coordinating multidisciplinary teams.
• Depth and breadth: Advanced capability in at least one role-defining pillar (Applied AI/ML; GenAI/RAG/agents; full-stack and integration engineering; or AI platform/MLOps) with credible breadth across the production lifecycle.
• Advanced RAG and agent systems: Hybrid or graph retrieval; knowledge graphs; source verification; MCP-style integration; durable or multi-agent workflows; policy enforcement; and adversarial testing.
• Preferred experience: Experience with Databricks, AWS, Spark, LLMs, and agentic development solutions is a plus.
• Domain consideration: Experience in life sciences, healthcare, payor, or…