Job VC

Description
Customer is a US medical Company that is dedicated to transforming patient’s lives at scale by being the driving force to decentralize the delivery of safe care into their homes or other preferred sites of care.
Project provides health systems with all that is needed to safely care for patients, including the clinical protocols, reimbursement model, platform technology, and fulfillment of all the clinical services required in the home through partners. Clinicians and patients broadly prefer this model over traditional care; both clinical and financial outcomes are improved as compared to traditional care.
Requirements
Must have:
Strong Python skills and solid Computer Science / software engineering fundamentals
Strong hands-on experience with LLMs and generative AI: building, integrating, and shipping LLM-powered applications to production (not traditional ML/model training)
Practical experience designing and building agentic workflows / AI agents (e.g., LangChain, LlamaIndex, Bot Framework, Claude/Bedrock agents, or similar)
Experience with agent and LLM evaluation: measuring quality, grounding, and reliability of AI solutions
REST APIs and integration of AI solutions with enterprise data sources (SharePoint, Confluence, Jira, etc.)
Delivery-focused seniority: a mature engineer able to own solutions end-to-end and grow into a team lead role
Excellent communication and collaboration skills: open-minded, comfortable in stakeholder meetings, works well in a remote setup
Nice to have:
Microsoft ecosystem experience: Copilot Studio / Power Platform, M365, Azure OpenAI
AWS Bedrock and Claude-based solutions (the client is actively rolling out Claude through Bedrock)
Some data engineering experience (pipelines, data preparation for RAG/grounding — no need to be a guru)
Experience in regulated environments (healthcare a strong plus)
Azure Functions, CI/CD pipelines, DevOps practices

Job responsibilities
Design, build, and productionize generative AI solutions and agentic workflows for company-wide use (engineering and non-engineering users)
Contribute to the three major AI engines currently in development, including supporting and pairing with existing team members
Build and run evaluations of AI agents: quality, grounding against private knowledge bases, and reliability
Ground AI solutions in enterprise knowledge sources (SharePoint, Confluence, Jira) with appropriate access and security controls
Write custom code (Python, APIs) to extend beyond low-code tool limitations where needed
Assess and advise on platform/tooling choices (Copilot Studio vs. Claude/Bedrock vs. custom), including cost and licensing trade-offs
Participate actively in stakeholder meetings; translate business needs into delivered AI solutions
Grow into a team lead role: set engineering direction, mentor AI enthusiasts on the team, and enable the sponsor to step back into a strategic role