Our offices in
Boca Raton, FL and
Nashville, TN are where we come together for
collaboration, education, and celebrations, and we're looking for teammates who can join us in person for those meaningful moments.
Reports to: VP, Data Products & Technology
Years Experience: 2-4 Years
Department: Data Solutions & Analytics
The Position:We're hiring a
mid-level AI Engineer to help build and scale the AI systems behind Violet and our broader analytics platform called ThirdBase. You'll work hands-on with large language models, retrieval systems, and healthcare data- building features that turn a governed warehouse of claims, referrals, HCP engagement, and campaign performance into trustworthy, compliant, natural-language analytics. You'll own well-defined components end-to-end and grow into larger systems, with support from senior engineers and clear technical direction.
The Responsibilities:Includes, but not limited to the following:
- Build and improve LLM-powered analytics features - agentic workflows, natural-language querying, retrieval-augmented generation (RAG), and tool-use over structured (SQL/Snowflake) and unstructured (document) data.
- Contribute to text-to-SQL and semantic-layer systems that let non-technical users query complex healthcare datasets accurately and safely.
- Develop retrieval pipelines over document stores (vector search, hybrid keyword + semantic).
- Implement evaluation, guardrails, and hallucination checks - accuracy on domain data is non-negotiable in healthcare.
- Help enforce data-governance and compliance controls in the AI layer (derived-insights-only, no PHI/PII exposure, appropriate access controls).
- Integrate multiple tools and data sources (warehouse, campaign platforms, web, document repositories) into agent workflows.
- Deploy and maintain services on AWS, and monitor model performance, latency, and cost.
- Collaborate with product, analytics, and senior engineers to translate requirements into shipped features.
The Essentials:Required- 2-4 years building production software, including some hands-on experience with LLM/GenAI applications (RAG, agents, or text-to-SQL) - through work, internships, or substantial personal projects.
- Solid Python and SQL; comfort querying a cloud data warehouse (Snowflake a plus).
- Working knowledge of AWS and deploying services in the cloud.
- Substantive experience in LangGraph, CrewAI, n8n, or other agentic frameworks
- Experience in AI-assisted coding and CICD processes.
- Familiarity working with Node, React, or other javascript frameworks
- Exposure to retrieval systems, embeddings, or vector search.
- Experiments-driven design using evaluation harnesses for change management
- Understanding of prompt engineering and LLM guardrails - a sense of how to make model outputs reliable, not just functional.
- Awareness of data privacy and compliance basics and willingness to build to them.
- Ability to own a feature or component end-to-end and collaborate across a team.
Nice to Have- Any experience with healthcare/life-sciences data (claims, referrals, HCP, ICD-10) or another regulated data domain.
- Familiarity with HIPAA / PHI-PII constraints or de-identified / derived-insights data models.
- Exposure to agent orchestration frameworks and multi-tool workflows.
- Basic MLOps/LLMOps: monitoring, cost/latency optimization.
- Background in analytics, BI, or data engineering.
Core Tech Stack:- Cloud: AWS (e.g., Lambda, S3, ECS/Fargate, API Gateway)
- Data warehouse: Snowflake (SQL, semantic layers)
- Languages: Python (primary), SQL
- AI/ML: LLMs (OpenAI/Anthropic-class models, incl. Amazon Bedrock), RAG, embeddings/vector search, prompt engineering, function-calling / tool-use, skills
- Data domains: healthcare claims, physician referrals, ICD-10 diagnosis, HCP engagement, campaign/media performance, survey/primary research
- Data & document tooling: vector databases, hybrid search, integrations with platforms like Egnyte and Google Drive
- Analytics libraries: pandas and modern data-analysis/plotting tooling
- Governance: compliance-aware data access, PII/PHI handling, source citation
