Platform Delivery Lead, Data & AI

Gilead Sciences•Raleigh, NC

7d•$146,200 - $189,200

About The Position

As part of our enterprise AI strategy to speed life-changing therapies to patients, we are seeking a Platform Delivery Lead to lead the technical development and delivery of the foundational Data and AI platform products that power AI/ML solutions across the enterprise. In this role, you will bridge the needs of platform consumers and use case teams with technical execution and manage platform engineering teams composed of internal resources and key external partners. You will drive technical excellence in our shared platform capabilities through hands-on architecture decisions, engineering quality standards and modern data and AI engineering practices, delivering reliable, secure, and scalable platform services from concept to scaled release. As the Platform Delivery Lead, Data & AI at Gilead you will ...

Requirements

Bachelor's Degree and Eight Years' Experience OR Masters' Degree and Six Years' Experience OR PhD and Two Years Experience
Significant experience leading technical delivery in an agile environment. Fluent with Agile/Scrum methodology and mindsets
Experience as a lead data engineer, platform engineer, or solution architect. Hands-on, full-stack experience with platform operations / continuous improvement in addition to design and implementation preferred
Strong technical understanding of AWS data and AI services (e.g., S3, EKS, Lake Formation, Bedrock) and the Databricks platform (e.g., Unity Catalog, Lakeflow/Delta Live Tables, model serving, databricks apps), including how these are assembled into enterprise-grade data and AI platforms
Experience building and operating shared, multi-tenant platform capabilities and self-service developer experiences
Experience working with large (15+) dispersed development teams
Product-centric mindset
Strong understanding of modern data and AI architectures, including data lakehouse, MLOps/LLMOps, API / Event driven architectures and agentic/AI service patterns
Familiarity with platform governance, security, and compliance considerations in a regulated environment preferred
Strong team leadership skills; able to lead and influence without authority. Excellent communications and stakeholder management skills.

Nice To Haves

Bachelor's or higher degree in data or computer science, and 8+ years of relevant experience (BA/BS) / 6+ years relevant experience (MA/MS/MBA)

Responsibilities

Own end-to-end delivery including timeline and technical quality of Data and AI platform products and capabilities, from ideation through production and ongoing operations
Apply a product-centric approach with clear ownership, roadmaps, service-level objectives, and platform adoption metrics
Oversee the development of scalable, reusable platform capabilities built on AWS and Databricks, exposed as self-service products to use case and tenant teams
Drive AI services enablement across the platform, delivering shared capabilities (model access, RAG and retrieval services, evaluation, guardrails, and orchestration) that let teams build and deploy AI solutions faster
Champion the creation of reusable assets and accelerators — agent templates, MCP tools and servers, prompt and evaluation libraries, and reference patterns — that accelerate the development and adoption of AI agents across the enterprise
Lead hybrid technical delivery teams of platform and data/AI engineers, drawing on technical experts within and beyond the team
Manage sprint planning, backlog prioritization and release schedules, extending into continuous improvement as both platform needs and the technology landscape evolve
Facilitate technical problem-solving across data engineering, AI/ML and agentic enablement, governance, and platform reliability domains
Ensure platform capabilities integrate cleanly with the broader data and AI ecosystem and provide reusable components and reference patterns (ingestion frameworks, feature/serving infrastructure, shared services)
Shape technical standards for platform architecture, data models, pipeline patterns, and automation workflows that consuming teams build upon
Maintain technical documentation, architecture references, and operational runbooks for platform services and data pipelines
Track delivery and platform health metrics: reliability and availability targets, platform adoption, time-to-onboard, and operational efficiency
Manage vendor relationships and outsourced delivery with co-source providers
Monitor platform performance and drive continuous improvement toward quantifiable targets, including cost efficiency, resilience, and engineering excellence