ml engineer in healthcare

Amgen’s AI Studio is an enterprise engine that transforms high-value business challenges into scalable AI products. The team partners with business stakeholders to identify opportunities, develop actionable use cases, and deliver responsible AI products across the full lifecycle.

• 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.

• Master’s degree and 4 to 6 years of Computer Science, IT or related field experience, or Bachelor’s degree and 6 to 8 years of experience, or Diploma and 9 to 10 years of experience;
• 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;
• 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;
• Nice to have: PyTorch, TensorFlow, Hugging Face, scikit-learn, XGBoost, PyMC, computer vision, NLP, GNNs, causal inference, uncertainty estimation, LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI, hybrid retrieval, knowledge graphs, graph RAG, evidence verification, AWS, Bedrock, SageMaker, Databricks, Spark, Kubernetes, infrastructure as code, MLflow, Airflow, Kubeflow, GitHub Actions, MCP-style integration, agent tracing, adversarial testing, durable workflows, permissions, human review, BI, process automation, healthcare, life sciences, GxP, validated systems or other regulated or high-impact environments.

• Office located in the heart of Lisbon, Portugal.