Principal, Agentic AI/Chatbots IC

Department Name: AI & Data Science

Role GCF: 6

ABOUT THE ROLE

Role Description:

We are seeking a Principal Machine Learning EngineerAmgens most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI solutions. Sitting at the intersection of engineering excellence and data-science enablement, you will develop, deploy and monitor modelsclassical ML, deep learning and LLMssecurely and cost-effectively. Acting as a player-coach, you will establishAIsolution strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI solutions.

Roles & Responsibilities:

• Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem.

• Build production ML/GenAI solutions and lightweight apps delivering sub-second insights.

• Build end-to-end ML pipelinesdata ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotionusing 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, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks.

• Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining.

• Architect LLM/RAG with prompt management, safety guardrails, and optimized inference.

• Enforce data quality, lineage, and model/data cards; apply privacy-preserving techniques where needed.

• Contribute reusable ML/GenAIcomponentsfeature stores, model registries, experiment-tracking librariesand 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.

• Prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.

• Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs.

Must-Have Skills:

• 5-7 years in AI/ML and enterprise software.

• Strong command of machine-learning algorithmsregression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniqueswith 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 business-case skillsable to model TCO vs. NPV and present trade-offs to executives.

• 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

• Published thought-leadership or conference talks on enterprise GenAI adoption.

• Masters degree in Computer Science and or Data Science

• Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.

Education and Professional Certifications

• Masters degree with years of experience in Computer Science, IT or related field

OR

• Bachelors degree with 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.