Senior AI Engineer

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Career Category Job Description Join our team at AMGEN Capability Center Portugal, consistently recognised among the top companies in the Best Workplaces(TM) ranking by Great Place to Work(R) in Portugal. 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 people's lives. We are seeking a Senior AI Engineer —Amgen's senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI platforms. Sitting at the intersection of engineering excellence and data-science enablement, you will design the core services, infrastructure and governance controls that allow hundreds of practitioners to prototype, deploy and monitor models—classical ML, deep learning and LLMs—securely and cost-effectively. Acting as a "player-coach," you will establish platform strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI developer experience. Roles & Responsibilities: - Engineer end-to-end ML pipelines —data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion—using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks. Engineer end-to-end ML pipelines —data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion—using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks. - Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes and exposing secure REST, gRPC or event-driven APIs for consumption by downstream applications. Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes and exposing secure REST, gRPC or event-driven APIs for consumption by downstream applications. - Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency. Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency. - Optimise performance and cost at scale —selecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantisation/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets. Optimise performance and cost at scale —selecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantisation/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets. - Instrument comprehensive observability —real-time metrics, distributed tracing, drift & bias detection and user-behaviour analytics—enabling rapid diagnosis and continuous improvement of live models and applications. Instrument comprehensive observability —real-time metrics, distributed tracing, drift & bias detection and user-behaviour analytics—enabling rapid diagnosis and continuous improvement of live models and applications. - Embed security and responsible-AI controls (data encryption, access policies, lineage tracking, explainability and bias monitoring) in partnership with Security, Privacy and Compliance teams. Embed security and responsible-AI controls (data encryption, access policies, lineage tracking, explainability and bias monitoring) in partnership with Security, Privacy and Compliance teams. - Contribute reusable platform components —feature stores, model registries, experiment-tracking libraries—and evangelise best practices that raise engineering velocity across squads. Contribute reusable platform components —feature stores, model registries, experiment-tracking libraries—and evangelise best practices that raise engineering velocity across squads. - Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness. Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness. - Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs. Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs. Must-Have Skills: - 3-5 years in AI/ML and enterprise software. 3-5 years in AI/ML and enterprise software. - Comprehensive command of machine-learning algorithms — regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques—with the judgement to choose, tune and operationalise the right method for a given business problem. Comprehensive command of machine-learning algorithms — regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques—with the judgement to choose, tune and operationalise the right method for a given business problem. - Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale. Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale. - Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel). Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel). - Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines). Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines). - Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives. Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives. - Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives. Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives. Good-to-Have Skills: - Experience in Biotechnology or pharma industry is a big plus Experience in Biotechnology or pharma industry is a big plus - Published thought-leadership or conference talks on enterprise GenAI adoption. Published thought-leadership or conference talks on enterprise GenAI adoption. - Master's degree in computer science and or Data Science Master's degree in computer science and or Data Science - Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. Soft Skills: - Excellent analytical and troubleshooting skills. Excellent analytical and troubleshooting skills. - Strong verbal and written communication skills Strong verbal and written communication skills - Ability to work effectively with global, virtual teams Ability to work effectively with global, virtual teams - High degree of initiative and self-motivation. High degree of initiative and self-motivation. - Ability to manage multiple priorities successfully. Ability to manage multiple priorities successfully. - Team-oriented, with a focus on achieving team goals. Team-oriented, with a focus on achieving team goals. - Ability to learn quickly, be organised and detail oriented. Ability to learn quickly, be organised and detail oriented. - Strong presentation and public speaking skills. Strong presentation and public speaking skills. APPLY NOW Objects in your future are closer than they appear. Join us. CAREERS.AMGEN.COM EQUAL OPPORTUNITY STATEMENT Amgen is an Equal Opportunity employer and will consider you without regard to your race, colour, 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 to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Salary Range