Lead AI Engineer

Trio Workforce Solutions

. Own the architecture of the Digital Worker platform and AI capabilities embedded in Trio's products .

Posted 9/21/2026full-timeRemote • United StatesSenior💰 $150,000 - $180,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI architecture, production LLM-powered systems, and agentic workflows, while effectively leading and mentoring engineering teams. Proficient in translating technical strategies into business objectives and ensuring high-quality AI system performance.

Highest-signal resume keywords

AI ArchitectureProduction LLM-Powered SystemsAgentic SystemsPython ProgrammingTeam Leadership

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Hard Skills

Software EngineeringData EngineeringMachine Learning FundamentalsDistributed SystemsAI System Quality EvaluationArchitectural StandardsTechnical Design ReviewsWorkflow ExecutionMonitoring and Logging StandardsEvaluation Frameworks

Soft Skills

Excellent CommunicationMentoringProblem-SolvingCollaborationDecision-Making

Tools & Technologies

Azure CloudAzure OpenAIEvent-Driven InfrastructureVector DatabasesSemantic SearchEmbeddingsAI GovernanceDatadogAzure DevOpsSaaS

Certifications & Qualifications

Bachelor's Degree in Computer ScienceMaster's Degree in Computer Science (Preferred)

Industry Keywords

HealthcareHigh-Growth Technology OrganizationsWorkforce TechnologyAI ComplianceResponsible AI

Tech Stack

Tools & technologies

AzureCloudDistributed SystemsPythonReactSQLTypeScript.NET

About the role

Key responsibilities & impact

Own the architecture of the Digital Worker platform and AI capabilities embedded in Trio's products
Establish design principles, engineering patterns, and architectural standards for AI systems
Lead technical design reviews and make tradeoff decisions on quality, latency, cost, and operational complexity
Evaluate emerging AI technologies and determine adoption priorities
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 team's hardest technical problems hands-on
Own standards for measuring AI quality, including accuracy, groundedness, safety, latency, and business impact
Build and extend evaluation frameworks to detect regressions and hallucinations
Drive measurable system-quality improvements through experimentation and production learning
Define monitoring, tracing, and logging standards for production AI systems
Establish reliability and performance objectives and serve as senior escalation for critical AI production issues
Improve production readiness and AI lifecycle management as the platform scales
Mentor the AI Engineering team and develop architectural judgment and engineering depth
Lead code and design reviews and enforce engineering standards
Partner with Product, Platform, Data, Security, and executive stakeholders
Translate business objectives into technical strategy and technical tradeoffs into business terms
Extend the Digital Worker framework, evaluation infrastructure, platform orchestration, centralized inference, and retrieval and knowledge systems
Expected to assume formal leadership of the AI Engineering team as it grows

Requirements

What you’ll need

Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field, or equivalent practical experience
7+ years in software engineering, data engineering, or a closely related technical field
2+ years building and operating production LLM-powered systems
Demonstrated depth in agentic systems, RAG architectures, or AI orchestration frameworks
Solid understanding of machine learning fundamentals
Strong distributed systems and software architecture fundamentals
Professional proficiency in Python; comfort in a polyglot environment
Track record of evaluating AI system quality quantitatively and acting on the results
Experience mentoring engineers on technical and architectural decisions
Excellent written and verbal communication with technical and executive audiences
Preferred: Master's degree in Computer Science, AI, Machine Learning, or a related technical discipline
Preferred experience with team leadership or management, Azure cloud, Azure OpenAI, event-driven infrastructure, vector databases, semantic search, embeddings, enterprise knowledge systems, agent orchestration platforms, AI governance/security/compliance/responsible AI, .NET/C#, React/TypeScript, SQL Server, CosmosDB, Auth0, Azure DevOps, Datadog, and SaaS, workforce technology, healthcare, or high-growth technology organizations
Ability to perform essential functions with or without reasonable accommodation
Ability to remain stationary for approximately 50% of the workday, learn and apply new tasks and procedures, maintain attention, work independently, make timely workflow-related decisions, and meet productivity and/or time-based performance expectations