Sr Manager, Data/AI Solution Architect

Key Responsibilities

• Solution design & delivery: Partner with function business process leads across HR, Compliance, and Legal to assess needs and translate them into practical AI solution designs and serve as the technical lead across solutions.
• Model development & tuning: Build, fine-tune, and evaluate AI/LLM models grounded in policy, procedural, and regulatory requirements, and adaptable to Gilead-specific content, internal materials, and approved templates.
• Knowledge base engineering: Design and maintain retrieval-augmented (RAG) knowledge bases incorporating authoritative external guidance, corporate policy libraries, compliance frameworks and codes of conduct, and Gilead-specific documentation.
• Review & workflow automation: Develop reusable capabilities for document review, summarization, consistency and policy-alignment checking, citation accuracy, and risk flagging — with traceable citations back to source content.
• Data pipelines: Engineer secure, scalable ingestion and processing pipelines for diverse content types across enterprise support functions, including policy and procedure documents, employee-facing communications, training and enablement materials, internal narratives, external publications, and structured business records.
• Delivery through extended teams: Direct execution through engineers, vendor partners, and shared platform resources — setting technical direction, reviewing designs, ensuring quality, and driving solutions to production at a scale beyond individual delivery.
• Evaluation & quality: Establish evaluation frameworks, accuracy benchmarks, guardrails, and human-in-the-loop validation to ensure trustworthy, defensible, and auditable outputs.
• Stakeholder collaboration: Act as the bridge between business stakeholders and AI delivery — converting functional review and knowledge-work needs into trusted, auditable solutions that reduce cycle time and organizational risk. Drive adoption, enablement, and change management.
• Governance: Ensure solutions meet data privacy, confidentiality, security, and responsible-AI standards, with role-based access appropriate to sensitive enterprise content.
• MLOps: Implement model deployment, monitoring, versioning, and continuous improvement practices, and establish reusable architecture standards and delivery patterns across the portfolio.

Basic Qualifications

Bachelor's Degree and Eight Years' Experience OR Master's Degree and Six Years' Experience

Preferred Qualifications

• 5+ years of experience in AI/ML engineering, with hands-on work building LLM/generative-AI applications (fine-tuning, RAG, prompt engineering).
• Strong programming skills in Python and modern ML frameworks.
• Experience designing knowledge bases and retrieval systems over large structured and unstructured sources.
• Experience with AWS cloud AI platforms and MLOps tooling.
• Solid data engineering fundamentals: pipelines, data modeling, and large-scale processing.
• Experience delivering a portfolio of solutions across multiple business functions, including leading delivery through engineering teams and external partners.
• Experience in pharmaceutical, biotech, life sciences, or other highly regulated industries.
• Familiarity with Tech policies, compliance, and governance workflows in a regulated enterprise environment.
• Experience with enterprise context build and automation
• Experience handling confidential and sensitive enterprise data, including HR and Legal with appropriate privacy controls.
• Experience with responsible AI, model governance, and validation in regulated environments.