Specialist Software Engineer (Full Stack)

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. </span