Lead AI Engineer

It's fun to work in a company where people truly believe in what they're doing! We're committed to bringing passion and customer focus to the business. Trio WFS builds Digital Workers, autonomous AI systems that execute real operational workflows in healthcare workforce management, already running in production and delivering measurable results for our clients.

The Lead AI Engineer is the senior technical authority for this work, responsible for driving the architecture, technical direction, and engineering standards that support Trio's AI-powered products, Digital Workers, and intelligent automation capabilities. This role leads the design and evolution of complex AI systems spanning agentic workflows, retrieval-augmented generation (RAG) architectures, orchestration frameworks, evaluation platforms, observability capabilities, and product AI infrastructure.

The Lead AI Engineer operates as the technical leader for the AI Engineering team, influencing architecture decisions across Trio's products while partnering closely with Product, Engineering, Data, Platform, and executive stakeholders. This role is being developed with a clear path toward team leadership. The ideal candidate combines deep technical expertise with a genuine desire to grow into people management, and will be coached and mentored to eventually lead the AI Engineering team.

This is a hands‑on technical leadership role on a small, high-leverage team of one Senior AI Engineer and one AI Engineer, with direct exposure to Engineering, Product, and executive leadership.

• The Digital Worker framework, a pattern where policies (the questions a client needs answered) combine with features (the data available to answer them) to produce grounded, auditable decisions.
• Extending this framework to new workflows is the core of the job.
• Evaluation infrastructure, the harness that lets us quantitatively measure whether a change to a prompt, retrieval strategy, or model actually improved anything. This gates what ships.
• Platform orchestration, an event-driven orchestrator with autoscaling, input-level quarantine, and per-worker circuit breaking.
• Centralized inference, an LLM gateway that routes, logs, and controls cost and latency across every AI-powered capability in the product.
• Retrieval and knowledge systems, ingestion, embedding, indexing, and grounding strategies that keep answers accurate and traceable.

• Architecture & Technical Direction Own the architecture of the Digital Worker platform and the AI capabilities embedded in Trio's products Establish design principles, engineering patterns, and architectural standards for AI systems Lead technical design reviews and make the tradeoff calls on quality, latency, cost, and operational complexity Evaluate emerging AI technologies and determine what is worth adopting
• AI Systems Engineering Design and build production systems using LLMs, retrieval architectures, agentic workflows, and tool use Build reusable frameworks for agent orchestration, tool integration, workflow execution, and memory Solve the hardest technical problems on the team, hands‑on
• Evaluation & Quality Own the standards for how we measure AI quality: accuracy, groundedness, safety, latency, and business impact Build and extend evaluation frameworks that catch regressions and hallucinations before clients do Drive measurable improvement in system quality through experimentation and production learning
• Observability & Operational Excellence Define monitoring, tracing, and logging standards for AI systems in production Establish reliability and performance objectives; serve as senior escalation for critical AI production issues Improve production readiness and AI lifecycle…