Specialist Software Engineer (Full Stack), AI Studio

Career Category

Information Systems

Job Description

CAREER LEVEL:   GCF 5 – Specialist

CAREER TRACK:   Individual Contributor

PRIMARY SCOPE:   End-to-end ownership of a small full-stack AI product or   substantial   technical 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.

ABOUT THE ROLE

Role Description:

The   Senior Full-Stack Software 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 define and own a small AI product or substantial application workstream from problem framing through architecture, implementation, launch, stabilization, support transition,   adoption   and measurable outcome.

You will remain hands-on while leading engineering decisions that turn   com ple x   business needs into secure, accessible,   reliable   and reusable products spanning user experience, APIs, distributed services, data and integrations, automation, ML, GenAI, RAG, bounded   agents   and human review. Within Applied AI, AI Studio turns prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value across software, data, automation, machine learning, Generative AI, RAG, bounded agents, evaluation,   observability   and lifecycle operations.

Roles & Responsibilities:

Define the user, workflow, intended use, baseline, acceptance criteria, accessibility needs, adoption path, operating owner, value   hypothesis   and measurable technical and business outcomes.

Map rules, handoffs, exceptions, data   dependencies   and human decision points before selecting conventional software, automation, ML, GenAI, RAG,   agents   or a manual approach.

Lead sprint and release planning, estimates, capacity, dependencies, MVP and phased roadmaps, explicit assumptions, total cost of ownership and support transition.

Own production architecture across human-AI experience, frontend, API gateway, backend services, persistence, events, integrations, data/knowledge flows, AI capabilities, security   zones   and human review.

Lead responsive frontend and backend delivery using modern JavaScript/TypeScript and Python, Node.js, Java or C#, with stable contracts, asynchronous workflows, retries, idempotency, compensation, rate   limits   and graceful degradation.

Integrate model services, LLMs, RAG, search, agents, BI and deterministic automation with access-aware data, validation, representative evaluation, human control,   feedback   and traceable evidence.

Establish risk-based testing, AI evaluation, CI/CD, policy gates, infrastructure as code, staged release, SLOs, telemetry, rollback, incident response, disaster recovery, capacity, cost,   runbooks   and support ownership.

Coordinate governance and   GxP   controls, create reusable components and playbooks, lead root-cause analysis, measure adoption and value, mentor engineers and improve delivery practices.

Basic Qualifications and Experience:

•  Bachelor’s/ Master’s   degree with 8 - 13 years of experience in Computer Science,   IT   or related field.

Functional Skills:

End-to-end full-stack and human-AI engineering: User journeys, accessible frontend architecture, state, forms, secure sessions, streaming, review, correction, approval, uncertainty, feedback,   telemetry   and recoverable failure.

Backend, API,   data   and distributed-systems architecture: Services, events, workflows, persistence, caches, queues, transactions, retries, idempotency, compensation, multi-tenancy, consistency, resilience, contracts, data   models   and enterprise integrations.

AI-enabled applications and automation: Production integration of predictive ML, foundation models, embeddings, vector/graph retrieval, RAG, structured output, bounded agents, document/vision capabilities, BI and deterministic workflows.

Testing, cloud,   DevSecOps   and reliability: Representative testing and AI evaluation, repository governance, architecture decisions, CI/CD, infrastructure as code, quality gates, staged release, SLOs, observability, incidents, rollback, disaster   recovery   and FinOps.

Security, Responsible   AI   and regulated delivery: Identity, authorization, least privilege, tenant isolation, secrets, OWASP, supply-chain controls, privacy, threat   modeling , prompt/tool safeguards, validation, auditability,   accessibility   and   GxP   evidence.

Must-Have Skills:

Demonstrated end-to-end ownership of at least one production full-stack application, digital product,   automation   or AI-enabled system that delivered a measurable outcome.

Strong hands-on   proficiency   in JavaScript or TypeScript, at least one backend language such as Python, Java, C# or Node.js, and SQL, with experience designing production services.

Ability to lead application and integration architecture, delivery, release,   operations   and support while partnering with ML specialists for deeper model, RAG, agent and   MLOps   decisions.

Good-to-Have Skills:

Advanced frontend and human-AI platforms: Experience with Next.js or comparable rendering patterns, design systems, micro-frontends where justified,   multimodal   or collaborative interfaces, advanced   accessibility   and safe agent approval experiences.

Distributed and enterprise integration: Experience with Kafka or equivalent event platforms, durable workflows, outbox or saga patterns, stream processing, service mesh, API   gateways   and large-scale integration architecture.

Advanced AI and automation engineering: Experience with vector or graph databases, graph or multimodal retrieval, model and agent gateways, MCP-style tool integration, durable agents, agent evaluation, process mining, UiPath or n8n.

Cloud,   platform   and developer experience: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless services, infrastructure as code,   OpenTelemetry , policy as code or internal developer platforms.

Regulated systems and technical breadth: Experience delivering   GxP -relevant,   validated , pharmaceutical,   healthcare   or other high-impact systems and evaluating emerging technologies pragmatically.

Soft Skills:

Product thinking and ability to connect architecture and delivery decisions to measurable user,   workflow   and business value.

Strong technical leadership, mentoring and ownership while   remaining   hands-on.

Excellent analytical judgment, troubleshooting and clear communication of evidence,   limitations   and trade-offs.

Cross-functional leadership with product, business, UX, architecture, ML,   platform   and control functions.

Pragmatism, resilience and continuous improvement through incidents, feedback,   reuse   and measured outcomes.

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your race,   color , 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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