Abacus Insights is transforming how data and AI work for health plans. Our mission is simple: make healthcare data usable, so the people responsible for care and cost decisions can act faster, with confidence.

As the AI and Data Platform for Payer Intelligence, we help health plans break down data silos to create a single, trusted data foundation. That foundation powers better decisions—so plans can improve outcomes, reduce waste, and deliver better experiences for members and providers alike. Backed by $100M from top investors, we're tackling big challenges in an industry that's ready for change. Our platform enables GenAI use cases by delivering clean, connected, and reliable healthcare data to support automation, prioritization, and decision workflows—and it's why we are leading the way.

Our innovation begins with people. We are bold, curious, and collaborative—because the best ideas come from working together. We embrace the thoughtful use of AI and automation to drive innovation and efficiency, and we look for individuals who are curious and adaptable—those excited to leverage emerging technologies to enhance how we work—while keeping human insight, connection, and our clients at the center of every decision.

Ready to make an impact? Join us and let's build the future together.

This is a contract to hire position.

The Senior Forward-Deployed AI Engineer is a senior individual contributor responsible for designing and building the AI and GenAI/agentic systems that power our platform on Snowflake, and for carrying that work directly into client environments. Hands-on Snowflake expertise is essential to this role. Databricks experience is a plus but not required.

This is a forward-deployed role: the person in this seat spends a meaningful share of their time working inside client environments, not just building systems that clients eventually use. Candidates must bring prior, evidenced experience doing this kind of work already.

The role's focus will shift over time. Initially, the work is centered on configuring the Snowflake semantic layer and delivering metrics and reporting off of it for clients. As that foundation matures, the role expands into agentic AI work, which depends directly on the semantic layer already being correctly modeled and configured.

On client engagements, this person is the senior-most AI technical voice, trusted to work through ambiguous problems and help shape the technical vision and roadmap for AI components. This is technical leadership, not engagement ownership or people management; someone else owns the engagement and its reporting, and this role does not have direct reports.

• AI/ML architecture and model strategy on Snowflake
• GenAI and agentic systems design
• Direct, hands-on client engagement and implementation

Unlike a purely internal engineering role, this position regularly puts you in front of clients: scoping their needs, implementing solutions in their environment, and troubleshooting alongside them. Candidates should bring demonstrated experience doing this kind of work already, not only an interest in doing it.

Your day to day

Semantic Layer & Metrics Reporting (initial focus)

• Design, configure, and maintain the Snowflake semantic layer (e.g. Cortex Analyst semantic views), modeling business logic and metrics definitions accurately against client requirements
• Build and deliver metrics and reporting for clients off of the configured semantic layer
• Work directly with client stakeholders to define metrics requirements, validate outputs against business logic, and resolve discrepancies
• Write efficient, production-grade SQL and Python to support semantic layer configuration, metrics pipelines, and reporting delivery

GenAI & Agentic Systems (as the role evolves)

• Design and implement retrieval-augmented generation (RAG) systems, agentic workflows, and MCP-based tooling that draw on the semantic layer as their source of truth
• Build and maintain evaluation harnesses to test and monitor AI/agent quality and performance
• Apply sound judgment on model selection, prompting strategy, and system design tradeoffs
• Own technical architecture and model strategy decisions for AI systems built on the Snowflake platform

Client-Facing / Forward Deployed Engineering

• Serve as the senior-most AI technical voice on client engagements, helping shape technical direction and roadmap for AI components, without owning the engagement or its reporting
• Work directly with client stakeholders to scope AI/ML use cases, translate business requirements into technical solutions, and set realistic expectations on scope and timeline
• Implement and configure solutions within client environments, adapting the platform to client-specific data and constraints
• Serve as the technical point of contact for clients during implementation, including troubleshooting issues live with client teams
• Manage client relationships and expectations with the same rigor applied to the technical work itself

Cross-Functional Collaboration

• Partner with product, data science, and operations teams to move AI/ML work from prototype to production
• Communicate technical tradeoffs clearly to both technical and non-technical audiences, internal and client-facing
• Contribute to team best practices for AI engineering and client implementation work

What you bring to the team

• 8+ years of software/AI engineering experience, including hands-on design of AI/ML systems
• Deep, hands-on experience with Snowflake, including Snowpark, Snowflake Cortex, and Snowflake ML
• Hands-on experience building and maintaining semantic layers on Snowflake (e.g. Cortex Analyst semantic views), including modeling business logic and metrics for AI/agent consumption
• Experience delivering metrics and reporting off of a semantic layer for clients, including validating outputs against business logic
• Demonstrated experience designing and building GenAI/agentic systems: RAG, MCP-based tooling, agent orchestration patterns, and evaluation harnesses
• A proven track record working directly with external clients or customers in an implementation, forward-deployed, or professional-services capacity, with specific engagements you personally owned, not only internal engineering experience
• Demonstrated ability to operate as the senior technical voice on ambiguous problems, with sound judgment on AI technical direction, without needing close direction
• Strong Python engineering skills, including API design and delivery of production AI/ML pipelines
• Strong communication skills, with demonstrated ability to translate technical tradeoffs for non-technical, client-facing audiences
• Comfort operating with a degree of ambiguity typical of client environments, and sound judgment on when to escalate versus resolve independently

What we would like to see, but not required

• Hands-on experience with Databricks, including Unity Catalog, Delta Lake, and MLflow
• Healthcare payer or provider claims data experience
• Experience with classical ML and with model fine-tuning
• Relevant certifications (e.g. SnowPro)

As a mission-led technology company helping to drive better healthcare outcomes, Abacus Insights believes that the best innovation and value we can bring to our customers comes from diverse ideas, thoughts, experiences, and perspectives. Therefore, we dedicate resources to building diverse teams and providing equal employment opportunities to all applicants. Abacus prohibits discrimination and harassment regarding race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

At the heart of who we are is a commitment to continuously and intentionally building an inclusive culture—one that empowers every team member across the globe to do their best work and bring their authentic selves. We carry that same commitment into our hiring process, aiming to create an interview experience where you feel comfortable and confident showcasing your strengths. If there’s anything we can do to support that—big or small—please let us know.

AI Use in Recruitment

We use AI-powered tools throughout our recruitment process. This includes Greenhouse's Real Talent Matching Technology, which helps prioritize applications based on job-related criteria, as well as additional AI tools used to support sourcing, interview preparation, and other recruiting tasks. We do not remove the human element—our recruiters and hiring teams review all applications and make all decisions related to candidate progression and hiring, regardless of which tools are used to support that process.

Identity and Location Verification

To protect the integrity of our hiring process, all interviews are conducted by live video with your camera on. Virtual or blurred backgrounds and headsets are not permitted absent an approved accommodation, and you should be in a well-lit, private space where your face and surroundings are clearly visible. Candidates who advance may also be asked to complete identity and location verification, which may include verifying your identity, work history, and confirming that you are located where your application states. Applications containing knowingly false or materially misrepresented information, including identity, location, or credentials, may be disqualified, and any offer may be rescinded if a material misrepresentation is discovered at any point in the process.

I f you need an accommodation for any part of the interview process, please let your recruiter know in advance.