Associate AI Engineer, OI&A

Amgen • Thousand Oaks, CA

13d • $81,467 - $110,219

About The Position

The Associate AI Engineer will join the technology operations and data enablement team within Amgen’s Obesity Intelligence & Analytics organization. The role delivers data science, software engineering, and generative AI solutions for commercial and non-commercial functions across the enterprise.

The position maintains technical continuity throughout solution delivery, partnering with business stakeholders and multidisciplinary teams to shape viable solutions, coordinate execution, make delivery trade-offs, remove blockers, and contribute hands-on to critical components. Its scope includes enterprise solution engineering, applied AI/ML, GenAI, RAG and agents, integration, evaluation, MLOps/LLMOps, security, governance, and production operations. Technical accountability complements, but does not replace, explicit product, business, compliance, and long-term support ownership.

Requirements

• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field; alternatively, a diploma with at least 2 years of experience in Computer Science, Engineering, Data Science, or a related field.
• Strong hands-on proficiency in Python and SQL, with experience designing or reviewing production software, APIs, services, data flows, evaluation pipelines, and enterprise integrations.
• Ability to translate complex business problems into technical designs, executable delivery plans, acceptance criteria, and production-readiness evidence while coordinating multidisciplinary teams.
• Advanced capability in at least one role-defining area: Applied AI/ML, GenAI/RAG/agents, full-stack and integration engineering, or AI platform/MLOps, with breadth across the production lifecycle.
• Self-starter who demonstrates critical thinking, sound judgment, ownership, resilience, and the ability to navigate ambiguity and deliver results with minimal oversight.
• Ability to provide hands-on technical leadership through influence and constructive challenge, and to communicate evidence, uncertainty, risks, trade-offs, limitations, and delivery status clearly.

Nice To Haves

• Enterprise solution architecture and integration experience 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 experience, including production Python and SQL, classical ML and NLP awareness, foundation-model integration, prompt and context management, RAG, structured output, provenance, citations, bounded tool use, permissions, recovery, and human control.
• Cloud, DevSecOps, and lifecycle operations experience, including cloud-native services, containers, CI/CD, infrastructure as code, versioning, observability, SLOs, staged release, rollback, incidents, disaster recovery, capacity, FinOps, runbooks, and MLOps/LLMOps.
• Experience with advanced RAG, knowledge, and agent systems such as hybrid or graph retrieval, knowledge graphs, source verification, MCP-style integration, durable or multi-agent workflows, policy enforcement, and adversarial testing.
• Experience with Databricks, AWS, Spark, LLMs, and agentic development solutions.
• Experience in life sciences, healthcare, payor, or pharmaceutical environments.

Responsibilities

• Lead discovery by clarifying business workflows, users, intended outcomes, value hypotheses, acceptance criteria, operational constraints, data readiness, integration dependencies, and production implications.
• Translate complex problems into solution designs, delivery plans, technical workstreams, estimates, milestones, dependencies, risks, acceptance criteria, release approaches, and support transitions.
• Build, prototype, review, or contribute to critical production components, 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, access controls, observability, and human review.
• Coordinate 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 for 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 releases, monitoring, logging, SLOs, rollback, recovery, runbooks, and controlled deployment; support early issue triage, stabilization, and transition to the operating owner.
• Communicate evidence, risks, trade-offs, and status; measure adoption and value; and turn delivery lessons into reusable components, accelerators, standards, documentation, and playbooks.

Benefits

• Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts.
• Discretionary annual bonus program, or a sales-based incentive plan for field sales representatives.
• Stock-based long-term incentives.
• Award-winning time-off plans.
• Flexible work models where possible.
• Career development opportunities.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level