AI Engineer (Machine Learning & Generative AI)

Join our team at AMGEN Capability Center Portugal consistently recognized among the top companies in the Best Workplaces(TM) ranking by Great Place to Work(R) in 2026 we were once again distinguished as one of the top Best Workplaces in the country (category 201-500 employees) reinforcing our commitment to an exceptional employee experience and workplace culture.

We are a team of over 500 talented individuals spanning more than 30 functions and areas of expertise and representing over 40 nationalities. Together we bring diverse perspectives and professional backgrounds to help shape the future of healthcare through innovation and technology.

This is your opportunity to explore a world of possibilities across areas such as Data & Analytics Digital Technology & Innovation Cybersecurity R&D Operations Global Distribution Finance Regulatory Affairs General & Administrative Human Resources and many more.

Located in the heart of Lisbon our AMGEN office fosters a culture of innovation excellence and purpose. Come thrive with us at AMGEN supporting our mission To Serve Patients.

What we do at AMGEN matters in peoples lives.

CAREER TRACK:Individual Contributor

PRIMARY SCOPE:Independent ownership of defined production ML/AI components

ORGANIZATION:Applied AI AI Studio

AI Engineer (Machine Learning & Generative AI)

ABOUT THE ROLE

Role Description:

TheMachine Learning Engineeroffers 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 Amgens enterprise engine for turning high-value business challenges into scalable AI products.We partner with key business partners across the company toidentifythe 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 will independently own defined production components within enterprise AI products and automation solutions.

Your remit may include a model or inference service data or knowledge pipeline retrievalcomponent agent tool evaluation module APIworkflowor monitoring capability. You will design release diagnose and support thecomponentwhile 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 evaluationobservabilityand lifecycle operations.

Roles & Responsibilities:

• Definecomponentboundaries 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 errorhandlingand 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 failurebehavior.

• Engineer batch or event-driven data document feature embedding label and evaluation pipelines with schema validation lineage provenance accesscontroland consistency checks.

• Define representative evaluation for model quality uncertainty retrieval grounding citations task success tool correctness safety latencycostand user impact.

• Release and support components using cloud services containers CI/CD versioning monitoring rollback incidentresponseand runbooks; lead diagnosis of moderately complex failures.

• Apply security privacy Responsible AI validation auditability human oversight and applicableGxPcontrols; contribute reusable assets and guide Associate engineers on familiar work.

Basic Qualifications and Experience:

• Masters degree and 4 to 6 years of Computer ScienceITor related field experience OR

• Bachelors degree and 6 to 8 years of Computer ScienceITor related field experience OR

• Diploma and 9 to 10 years of Computer ScienceITor related field experience

Functional Skills:

• Production software and AI/ML system design: Python and SQL modules APIs background jobs event flows testing performance observability sourcecontroland maintainable failure semantics.

• Statisticsmodelingand experimentation: EDA feature engineering classification regression clustering ensembles cross-validation leakage prevention calibrationuncertaintyand decision-aware error analysis.

• GenAI RAGknowledgeand agents: Prompt and context management structured output chunking embeddings hybrid retrieval reranking citations access-aware retrieval toolschemasand human approval.

• Dataknowledgeand 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 andMLOps/LLMOps: Representative measures versioning CI/CD release gates quality and drift monitoring incidents rollback runbookscomponentsupportand lifecycle traceability.

• Responsible and regulated delivery: Least privilege privacy prompt-injection safeguards bias and robustness checks intended-use documentation human oversightauditabilityandGxPevidence.

Must-Have Skills:

• Demonstrated ownership of at least one production software data ML GenAI or automationcomponent.

• Strong hands-onproficiencyin Python and SQL with sound software-engineering and testing practices.

• Strong capability in at least one of classical ML GenAI/RAG/agentsorMLOps/platform engineering with working knowledge of adjacent areas.

Good-to-Have Skills:

• Advanced ML and deep learning: Experience withPyTorch TensorFlow Hugging Face scikit-learnXGBoostPyMC computer vision NLP GNNs causal inference or uncertainty estimation.

• Advanced GenAI and knowledge systems: Experience withLangChainLangGraphLlamaIndex Semantic KernelAutoGenCrewAI hybrid retrieval knowledge graphs graphRAGor evidence verification.

• Cloud data andMLOps: Experience with AWS Bedrock or SageMaker Databricks Spark Kubernetes infrastructure as codeMLflow 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 sciencesGxP 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-offsrisksand support implications.

• Strong collaboration with business SMEs product architecture software data platformevaluationand control partners.

• Ownershipreliabilityand disciplined follow-through from design through production support.

• Ability to guide junior engineers and learn new tools through evidence-based experimentation.

APPLY NOW

Objects in your future are closer than they appear. Join us.

EQUAL OPPORTUNITY STATEMENT

Amgen is an Equal Opportunity employer and will consider you without regard to your racecolour religion sex sexual orientation gender identity national origin protected veteran status disability status or any other basis protected by applicable law.

We will ensure that individuals with disabilities are provided with reasonable accommodation toparticipatein the job application or interview process to perform essential job functions and to receive other benefits and privileges of employment.

Salary Range