Amgen

24 сен

AI Engineer

выше рынка на 30,9%

вакансия 399 044 ₽

в среднем 304 891 ₽

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описание

Career Category

Information Systems

задачи

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 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.

Engineer end-to-end ML pipelines—data ingestion, feature engineering, training, hyper-parameter optimization, 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.

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.

Optimize performance and cost at scale—selecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantization/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-behavior 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.

Contribute reusable platform components—feature stores, model registries, experiment-tracking libraries—and evangelize 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.

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.

требования

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 judgment to choose, tune and operationalize 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.

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; containerization (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.

Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.

Experience in Biotechnology or pharma industry is a big plus

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 Scaled Agile Framework (SAFe) for project delivery.

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, with a focus on achieving team goals.

Ability to learn quickly, be organized and detail oriented.

Strong presentation and public speaking skills.

APPLY NOW

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

CAREERS.AMGEN.COM

условия

51 065,45 EUR - 69 088,55 EUR

навыки

llm python java docker aws azure gcp devops mlops github agile security kubernetes rest grpc

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