Senior Forward Deployed Engineer (Sr ML Engineer)

Applied AI | AI Studio

ABOUT AMGEN

Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier,   fuller   and longer. We discover, develop,   manufacture   and deliver innovative medicines to help millions of patients. Amgen helped   establish   the biotechnology industry more than 40 years ago and   remains   at the   cutting edge   of innovation, using technology and human genetic data to push beyond what is known today.

ABOUT THE ROLE

Role Description:

The Senior Forward Deployed Engineer   offers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) - organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high-value business challenges into scalable AI products. We partner with key business partners across the company to   identify   the right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact.   Y ou will   be part of AI Studio   lead ing   the technical delivery of complex AI and automation solutions through   discovery , solution design, build, evaluation, production deployment, early stabilization and measurable value ,   pro duction deployment, early   stabilization   and measurable value.

You will   maintain   technical continuity across the lifecycle, 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. The role combines 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.

Roles & 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   the integrated architecture across applications, workflows, data and knowledge pipelines, models, retrieval, agents, APIs, enterprise integrations, identity, access controls,   observability   and human review.

• Orchestrate delivery across full-stack 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 behaviour with explicit release thresholds.

• Coordinate production readiness through CI/CD, staged release, 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 clearly; measure adoption and value and convert delivery lessons into reusable components, accelerators, standards,   documentation   and playbooks.

Basic Qualifications and Experience:

• Bachelor’s / Master’s   degree with 8 - 1 3   years of experience in Computer Science,   IT   or related field .

Functional Skills:

• Technical discovery, and value framing: Workflow analysis, intended-use definition, feasibility assessment, data and integration readiness, success measures, estimates, dependency   mapping   and technical go/no-go recommendations.

• 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; 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.

• Evaluation,   quality   and regulated delivery: Representative evidence, baselines, gold sets, error taxonomies, expert adjudication, model and retrieval quality, task success, safety, latency, reliability, failure analysis, Responsible AI, privacy, validation,   auditability   and   GxP   controls.

• 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 .

Must-Have Skills:

• Demonstrated end-to-end technical ownership of at least one production AI, ML, software,   data   or automation solution that delivered a measurable enterprise outcome.

• Strong hands-on   proficiency   in Python and SQL, with experience designing or reviewing production software, APIs, services, data flows, evaluation   pipelines   and enterprise integrations.

• Proven ability to turn complex business problems into coherent technical designs, executable delivery plans, acceptance   criteria   and production-readiness evidence while coordinating multidisciplinary teams.

• Advanced capability in at least one role-defining pillar—Applied AI/ML, GenAI/RAG/agents, full-stack and integration engineering, or AI platform/ MLOps —plus credible breadth across the production lifecycle.

Good-to-Have Skills:

• Advanced RAG,   knowledge   and agent systems: Hybrid or graph retrieval, knowledge graphs, source verification, MCP-style integration, durable or multi-agent workflows, policy   enforcement   and adversarial testing.

• Cloud,   data   and AI platforms: AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless or event-driven systems, infrastructure as code,   MLflow , Airflow, Kubeflow, observability and FinOps.

• Full-stack,   workflow   and automation breadth: JavaScript or TypeScript, modern web applications, API gateways, distributed workflows, process automation, document or   vision capabilities and human-AI review experiences.

• Regulated delivery and capability building: Life sciences, biotechnology, pharmaceutical, healthcare,   GxP   or   validated -system experience; reusable frameworks, accelerators, standards, platform   capabilities   and mentoring.

Soft Skills:

• Excellent critical thinking and ability to create clarity,   structure   and forward momentum in ambiguous situations.

• Strong technical leadership through influence, credibility, constructive   challenge   and hands-on problem solving.

• Clear communication of evidence, uncertainty, risks, trade-offs,   limitations   and delivery status to diverse audiences.

• Sound judgment, ownership and resilience when balancing value, speed, quality, security, compliance, cost,   maintainability   and supportability across global teams.

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race, colour, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.

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