AI Engineer - GenAI, RAG & MLOps (Hybrid)

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

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