Roles & Responsibilities
Own enterprise AI/ML architecture, engineering standards, APIs, guardrails and reusable patterns across cloud and on-prem environments.
Build production ML/GenAI solutions and lightweight applications that deliver business-ready insights with performance, reliability and usability in mind.
Build 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.
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.
Establish observability and operational excellence through SLOs, safe deployment patterns (blue-green/canary, shadow, rollbacks), incident runbooks and production monitoring.
Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining.
Architect LLM/RAG solutions with prompt management, grounding strategies, safety guardrails, evaluation frameworks and optimized inference patterns.
Enforce data quality, lineage and responsible AI practices by maintaining model/data cards, privacy controls, traceability, auditability and human oversight where required.
Contribute reusable ML/GenAI platform components—feature stores, model registries, experiment-tracking libraries, evaluation assets and deployment templates—and evangelize best practices that raise engineering velocity across teams.
Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness.
Prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.
Translate domain needs across R&D, Manufacturing and Commercial functions into technical roadmaps; influence solution design, mentor engineers and data scientists, and communicate trade-offs clearly to senior stakeholders.
Lead architecture reviews and technical governance for enterprise AI initiatives, making sound build-vs-buy decisions and ensuring alignment with security, compliance and platform standards.
Drive adoption in regulated environments by embedding validation readiness, explainability, risk controls and scalable operating practices into AI solutions from design through production.
Must-Have Skills
Overall career span of 10-12+ years building production-grade technology solutions with 3-5 years of hands-on experience in AI/ML and enterprise software.
Strong 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, LangGraph, 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 ability to evaluate technical and business trade-offs, including cost, scalability, risk, ROI and time-to-value, and present recommendations to senior stakeholders.
Exceptional stakeholder management and technical leadership; able to translate complex concepts into concise, outcome-oriented narratives and influence decisions across engineering, product and business teams.
Good-to-Have Skills
Experience in Biotechnology or pharma industry is a big plus
Exposure to Value and Access, market access, payer, commercial analytics or related healthcare business domains is preferred.
Experience delivering AI/ML solutions in regulated environments with familiarity in validation, auditability, explainability and risk controls is a 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.
Education and Professional Certifications
Master’s degree with 10-12 + years of experience in Computer Science, IT or related field
OR
Bachelor’s degree with 12-14 + years of experience in Computer Science, IT or related field
Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.
Soft Skills
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.
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
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. Please contact us to request an accommodation.
.
