Your remit may include a model or inference service, data or knowledge pipeline, retrieval component, agent tool, evaluation module, API, workflow or monitoring capability. You will design, release, diagnose and support the component while connecting technical measures to user and workflow outcomes. 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 component boundaries, intended use, acceptance criteria, non-functional requirements, decision consequences, support expectations and technical estimates with product and architecture partners.
Design and implement maintainable Python, SQL, API, data, model, retrieval, agent-tool and workflow components with clear contracts, configuration, testing, error handling and documentation.
Apply EDA, feature engineering, supervised or unsupervised methods, baselines, cross-validation, leakage prevention, calibration, subgroup, threshold, explainability and error analysis where relevant.
Build GenAI, NLP, RAG and bounded agent components using structured output, embeddings, hybrid search, reranking, provenance, citations, permissions, approvals, retries and recoverable failure behavior.
Engineer batch or event-driven data, document, feature, embedding, label and evaluation pipelines with schema validation, lineage, provenance, access control and consistency checks.
Define representative evaluation for model quality, uncertainty, retrieval, grounding, citations, task success, tool correctness, safety, latency, cost and user impact.
Release and support components using cloud services, containers, CI/CD, versioning, monitoring, rollback, incident response and runbooks; lead diagnosis of moderately complex failures.
Apply security, privacy, Responsible AI, validation, auditability, human oversight and applicable GxP controls; contribute reusable assets and guide Associate engineers on familiar work.
Basic Qualifications and Experience
Bachelor’s/Master’s degree and 5 to 9 years of Computer Science, IT or related field experience.
Functional Skills
Production software and AI/ML system design: Python and SQL modules, APIs, background jobs, event flows, testing, performance, observability, source control and maintainable failure semantics.
Statistics, modeling and experimentation: EDA, feature engineering, classification, regression, clustering, ensembles, cross-validation, leakage prevention, calibration, uncertainty and decision-aware error analysis.
GenAI, RAG, knowledge and agents: Prompt and context management, structured output, chunking, embeddings, hybrid retrieval, reranking, citations, access-aware retrieval, tool schemas and human approval.
Data, knowledge and cloud-scale systems: Batch and event-driven pipelines, contracts, lineage, provenance, relational/document/graph/vector stores, APIs, containers, Spark or Databricks and cloud-native services.
AI evaluation and MLOps/LLMOps: Representative measures, versioning, CI/CD, release gates, quality and drift monitoring, incidents, rollback, runbooks, component support and lifecycle traceability.
Responsible and regulated delivery: Least privilege, privacy, prompt-injection safeguards, bias and robustness checks, intended-use documentation, human oversight, auditability and GxP evidence.
Must-Have Skills
Demonstrated ownership of at least one production software, data, ML, GenAI or automation component.
Strong hands-on proficiency in Python and SQL, with sound software-engineering and testing practices.
Strong capability in at least one of classical ML, GenAI/RAG/agents or MLOps/platform engineering, with working knowledge of adjacent areas.
Good-to-Have Skills
Advanced ML and deep learning: Experience with PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, PyMC, computer vision, NLP, GNNs, causal inference or uncertainty estimation.
Advanced GenAI and knowledge systems: Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, hybrid retrieval, knowledge graphs, graph RAG or evidence verification.
Cloud, data and MLOps: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, infrastructure as code, MLflow, Airflow, Kubeflow or GitHub Actions.
Agents and human-AI workflows: Familiarity with MCP-style integration, agent tracing, adversarial testing, durable workflows, permissions, human review, BI or process automation.
Regulated enterprise delivery: Experience in healthcare, life sciences, GxP, validated systems or another regulated or high-impact environment.
Soft Skills
Independent problem solving and sound component-level technical judgment.
Clear communication of assumptions, evidence, trade-offs, risks and support implications.
Strong collaboration with business SMEs, product, architecture, software, data, platform, evaluation and control partners.
Ownership, reliability and disciplined follow-through from design through production support.
Ability to guide junior engineers and learn new tools through evidence-based experimentation.
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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