PRIMARY SCOPE: End-to-end technical delivery ownership of a small AI or automation solution or substantial deployment workstream
ORGANIZATION: 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.
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. You will be part of AI Studio leading the technical delivery of complex AI and automation solutions through discovery, solution design, build, evaluation, production deployment, early stabilization and measurable value, production 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:
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- 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 - 13 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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