Consulting Agentic AI Software Engineer

The Staff Agentic AI Engineer will provide crucial engineering leadership in the application of advanced Generative AI, supporting the revolutionization of healthcare by designing and implementing cutting-edge AI solutions. The Staff Agentic AI Engineer will be instrumental in developing systems that leverage Generative AI, particularly with GCP Vertex AI, Gemini, and related tooling, to create transformative products for users across the company’s 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 Staff Agentic AI Engineer should be a curious, adaptable, and forward-thinking engineer, comfortable navigating diverse technical ecosystems. The Staff Agentic AI Engineer will provide technical mentorship and foster a collaborative environment, translating complex AI concepts into actionable strategies for business stakeholders, product managers, and development teams.

• 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 the company’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.

• 8-10+ years of experience as a Software Developer or Engineer, moved into Solutions Architecture (Not Data Engineer)

• Recent 2-3 years understanding and practical experience in designing and building Agentic AI - Retrieval-Augmented Generation (RAG) architectures (NOT ML/Ops Models)

• Design, extend, and operationalize AI creation tools and intelligent agents leveraging advanced models.

• Secure, deploy, and productionize AI agents to ensure scalability, reliability, and compliance.

• Build or integrate Model Context Protocols (MCP) to support robust agent-model interactions. Develop agentic capabilities tailored to specific use cases, workflows, and functional requirements.

• Hands-on experience with Google Cloud Platform Vertex AI, or Azure AI Foundry, or Foundational Models

• Experience building cloud-native solutions using serverless and/or container-based architecture

• Full-stack working experience with Python, Java, C#, Node.js, SQL/NoSQL database, ETL/data pipelines, messaging queues/event streaming, docker/Kubernetes, microservices - Need at least 4 to proceed * Delivered Generative AI applications into production at scale in a customer-facing or enterprise environment

• Familiarity with Agentic AI concepts and frameworks (e.g., LangChain) for building autonomous and multi-agent systems