Sr Staff Agentic AI Engineer

Sr Staff Agentic AI Engineer

Remote, USA

Classification: Contract-to-hire

Contract Length: 5-12+ Months

Position Summary

The Sr Staff Agentic AI Engineer will be part of the team in Nashville, TN or Remotely. As a Sr Staff Agentic AI Engineer, you will be at the forefront of this mission, providing crucial engineering leadership in the application of advanced Generative AI. This is not just a software engineering role; it is an opportunity to revolutionize healthcare by designing and implementing cutting-edge AI solutions. You will be instrumental in developing systems that leverage Generative AI, particularly with Google Cloud Platform Vertex AI, Gemini, and related tooling, to create transformative products for users across HCA's nationwide ecosystem. This role will focus on practical applications of AI, including Retrieval-Augmented Generation (RAG), the design of sophisticated grounding data structures, and the construction of robust pipelines for grounding and embedding.

This position is within a flagship innovation team. The ideal candidate will be a curious, adaptable, and forward-thinking engineer, comfortable navigating diverse technical ecosystems. You will provide technical mentorship and foster a collaborative environment, translating complex AI concepts into actionable strategies for business stakeholders, product managers, and development teams.

Responsibilities

• Lead the design, development, and implementation of scalable, enterprise-grade Generative AI solutions on the Google Cloud Platform, with a strong focus on the Vertex AI suite.
• Architect and build sophisticated Retrieval-Augmented Generation (RAG) systems to ground large language models in HCA's proprietary and domain-specific data, ensuring response accuracy and relevance
• Design and implement efficient data pipelines for preparing, processing, and embedding data for use in Vector Stores like Vertex AI Vector Search
• Develop and apply innovative data grounding strategies and design the necessary data structures to connect LLMs with real-world, real-time information sources
• Spearhead the exploration and implementation of Agentic AI concepts to create autonomous systems capable of complex reasoning and task execution.
• Champion and integrate the Model Context Protocol to standardize communication between LLMs and external tools, enhancing system capabilities and interoperability.
• Promote a collaborative team environment and work closely with colleagues to achieve business objectives.
• Collaborate with stakeholders (e.g., business stakeholders, product owners, project managers, and end users) to understand functional and non-functional requirements for AI systems.
• Lead investigations and solution proposals for complex development and design problems in the AI domain.
• Guide team members in the scope of work estimation and forecasting for AI-related projects.
• Perform proof-of-concept and proof-of-technology tasks to secure buy-in from cohorts and stakeholders for new AI initiatives.
• Collaborate with team members to successfully execute development initiatives using Agile practices and principles.

Requirements

• Bachelor's degree Computer Science or related field
• 8+ years of experience a Software development or engineering roles
• Demonstrable experience in delivering Generative AI applications to production in a customer-facing or enterprise environment.
• Previous experience delivering Generative AI applications into production at scale in a customer-facing or enterprise environment
• Hands-on experience with Google Cloud Platform Vertex AI, or Azure AI foundry, or Foundational Models. Google Cloud Platform preferred.
• Previous experience designing and implementing Retrieval-Augmented Generation systems
• Experience designing grounding strategies or data structures to connect LLMs with enterprise or realtime information sources
• Experience building or supporting data pipelines for embeddings and vector stores
• Experience building cloud-native solutions using serverless and/or container-based architecture