Senior Forward Deployed Engineer, AI Studio

About the role

Amgen is hiring a Senior Forward Deployed Engineer for its AI Studio, within the AI & Data Science organization. The engineer leads the technical delivery of complex AI and automation solutions, from discovery and solution design through production deployment, early stabilization and measurable value. The role combines enterprise solution engineering, applied AI/ML, GenAI, RAG and agents, integration, evaluation, MLOps/LLMOps, security, governance and production operations.

Key responsibilities

• Lead discovery by clarifying the business workflow, users, intended outcome, value hypothesis, acceptance criteria, data readiness, integration dependencies and production implications.
• Turn complex problems into an executable solution design, delivery plan, technical workstreams, estimates, milestones, risks, release approach and support transition.
• Build, prototype, review or contribute to critical production components such as AI-enabled applications, RAG, bounded agents, intelligent automation, APIs and integrations.
• Maintain the integrated architecture across applications, workflows, data and knowledge pipelines, models, retrieval, agents, APIs, identity, access controls, observability and human review.
• Orchestrate delivery across full-stack engineering, data science, ML, testing, platform, security, compliance and business roles.
• Set up integrated testing, AI evaluation and governance covering functional, performance, security, data, model, retrieval, generation, tool-use, human oversight and operational behaviour, with explicit release thresholds.
• Coordinate production readiness through CI/CD, staged release, monitoring, logging, SLOs, rollback, recovery, runbooks and controlled deployment, supporting early issue triage and stabilization.
• Communicate evidence, risks and trade-offs, measure adoption and value, and turn delivery lessons into reusable components, standards, documentation and playbooks.

Required profile

• Basic: doctorate with 1 year of experience, or master's with 8-10 years, or bachelor's with 10-12 years, or diploma with 12-14 years in Computer Science, IT or a related field.
• Demonstrated end-to-end technical ownership of at least one production AI, ML, software, data or automation solution with a measurable enterprise outcome.
• Proven ability to turn complex business problems into technical designs and delivery plans while coordinating multidisciplinary teams.
• Advanced capability in at least one pillar (Applied AI/ML, GenAI/RAG/agents, full-stack and integration engineering, or AI platform/MLOps) plus breadth across the production lifecycle.
• Strong technical leadership, clear communication of evidence and risks, and sound judgment balancing value, speed, quality, security, compliance, cost and maintainability.

Required skills

• Production Python and SQL, classical ML and NLP awareness, foundation-model integration, RAG, prompt and context management, structured output, provenance and citations.
• Evaluation and quality: baselines, gold sets, error taxonomies, model and retrieval quality, latency, reliability, Responsible AI, privacy, validation, auditability and GxP controls.
• Cloud, DevSecOps and lifecycle operations: cloud-native services, containers, CI/CD, infrastructure as code, observability, SLOs, staged release, rollback, incidents, disaster recovery, FinOps, runbooks and MLOps/LLMOps.
• Advanced RAG, knowledge and agent systems: hybrid or graph retrieval, knowledge graphs, source verification, MCP-style integration, multi-agent workflows, policy enforcement and adversarial testing.
• Platforms: AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless, MLflow, Airflow, Kubeflow.
• Full-stack and automation: JavaScript or TypeScript, API gateways, distributed workflows, process automation, document or vision capabilities and human-AI review.
• Regulated delivery: life sciences, biotechnology, pharmaceutical, healthcare, GxP or validated-system experience.