Associate AI Engineer, OI&A

DataJobs; Thousand Oaks (CA); Hybrid; USD 81,000 - 110,000; Full time

Benefits offered by this job

Retirement and Savings Plan

Medical, dental & vision coverage

Life and disability insurance

Flexible spending accounts

Discretionary annual bonus program

Stock-based long-term incentives

Award-winning time-off plans

Flexible work models where possible

Job summary

Amgen is seeking an Associate AI Engineer focused on delivering end-to-end data science, software engineering, and GenAI solutions within a hybrid work model in Thousand Oaks, CA. You will lead integrated solution engineering across AI/ML, RAG, agents, and MLOps, from discovery through production with governance and measurable adoption.

The role emphasizes translating business needs into executable designs, coordinating multiple teams, and ensuring robust security, observability, and deployment

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science or related field OR Diploma with 2+ years of experience
• Experience with end-to-end AI/ML and GenAI engineering in production environments
• Strong Python and SQL, production software design/review experience

Responsibilities

• Lead integrated end-to-end solution engineering across AI/ML, RAG and agents, MLOps and LLMOps
• Define and maintain integrated architecture across applications, workflows, data and knowledge pipelines
• Coordinate production readiness through CI/CD, staged release, monitoring, and runbooks
• Communicate risks, trade-offs, and status clearly and drive reusable accelerators

Skills

Python

SQL

Databricks

AWS

LLMs

CI/CD

MLOps/LLMOps

Problem solving

Data governance

Education

Bachelor’s degree in Computer Science or related field

Diploma with 2+ years of experience

Tools

GenAI tools

APIs

RAG/agents

Job description

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 and Work Model

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

Compensation

• Salary range: USD 81,466 - 110,219 per year

Role Summary

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.

Responsibilities

• 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 workstreams, 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.

Requirements

• 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, DevSecOps and lifecycle operations: Cloud-native services; containers; CI/CD; infrastructure as code; versioning; observability; SLOs; staged release; rollback; incidents; disaster recovery; capacity; FinOps; 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 pharmaceutical environments will be factored in.
• Working style: Self-starter mentality with critical thinking, sound judgment, ownership, resilience in ambiguity, hands-on technical leadership through influence and constructive challenge, and clear communication of evidence, uncertainty, risks, trade-offs, limitations, and delivery status.

Technology Stack

• Python
• SQL
• Databricks
• AWS
• Spark
• LLMs
• CI/CD
• Infrastructure as code
• MLOps/LLMOps

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 for field sales representatives, a sales-based incentive plan
• Stock-based long-term incentives
• Award-winning time-off plans
• Flexible work models where possible

What You Will Do

• Deliver advanced data science, software engineering, and GenAI solutions enabling commercial and non-commercial functions across the enterprise.
• Maintain technical continuity across the lifecycle by working with business stakeholders and multidisciplinary teams to shape the simplest viable solution, coordinate execution, make delivery trade-offs, remove blockers, and contribute hands-on to critical components.
• Apply enterprise solution engineering across applied AI/ML, GenAI, RAG and agents, integration, evaluation, MLOps/LLMOps, security, governance, and production operations.
• Provide technical accountability that complements explicit product, business, compliance, and long-term support ownership.

Additional Salary Information

The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on relevant skills, experience, and qualifications.