ml engineer for enterprise AI platforms

Amgen is a biotechnology and pharmaceutical manufacturing company that develops healthcare solutions through innovation and technology. Its AMGEN Capability Center Portugal supports areas including data and analytics, digital technology, cybersecurity, research and development operations, global distribution, finance, and regulatory affairs.

• Engineer end-to-end ML pipelines covering data ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration, and automated promotion;
• Harden research code into production-grade microservices, package models in Docker/Kubernetes, and expose secure REST, gRPC, or event-driven APIs;
• Build and maintain full-stack AI applications by integrating model services with UI components, workflow engines, or business-logic layers;
• Optimize performance and cost at scale by selecting algorithms, applying quantization and pruning, and tuning GPU/CPU auto-scaling policies;
• Instrument observability with real-time metrics, distributed tracing, drift and bias detection, and user-behavior analytics;
• Embed security and responsible-AI controls, including data encryption, access policies, lineage tracking, explainability, and bias monitoring;
• Contribute reusable platform components such as feature stores, model registries, and experiment-tracking libraries;
• Evangelize engineering best practices across squads;
• Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets;
• Partner with data scientists to prototype and benchmark algorithms, guide scalability and production-readiness decisions, and co-own model-performance KPIs;
• Establish platform strategy and define technical standards;
• Partner with DevOps, Security, Compliance, and Product teams to deliver an enterprise-grade AI developer experience.

• 3–5 Years of experience in AI/ML and enterprise software;
• Comprehensive knowledge of machine-learning algorithms, including regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, CNNs, RNNs, transformers, LLM, and RAG techniques;
• Experience selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale;
• Expert knowledge of GenAI tooling, including vector databases, RAG pipelines, prompt-engineering DSLs, and agent frameworks such as LangChain and Semantic Kernel;
• Proficiency in Python and Java;
• Experience with Docker/K8s, AWS, Azure or GCP, and modern DevOps/MLOps tools including GitHub Actions and Bedrock/SageMaker Pipelines;
• Strong business-case skills, including modeling TCO versus NPV and presenting trade-offs to executives;
• Exceptional stakeholder management and ability to explain complex technical concepts through concise, outcome-oriented narratives;
• Master's degree with 8+ years of experience in Computer Science, IT, or a related field, or Bachelor's degree with 10+ years of experience in Computer Science, IT, or a related field;
• Excellent analytical and troubleshooting skills;
• Strong verbal and written communication skills;
• Ability to work effectively with global, virtual teams;
• High degree of initiative and self-motivation;
• Ability to manage multiple priorities successfully;
• Team-oriented approach focused on achieving team goals;
• Ability to learn quickly, stay organized, and remain detail-oriented;
• Strong presentation and public-speaking skills;
• Nice to have: Experience in Biotechnology or pharma, published thought leadership or conference talks on enterprise GenAI adoption, Master's degree in computer science and/or Data Science, familiarity with Agile methodologies and SAFe, certifications on GenAI/ML platforms such as AWS AI, Azure AI Engineer, or Google Cloud ML.

• No conditions specified