You will remain hands-on while leading engineering decisions that turn complex 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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