AI Software Lead

About the role

This is not a traditional software engineering role, and "Lead" does not mean managing people. You will lead agent-driven software delivery: deciding what to build, decomposing work for AI coding agents, supervising their output, validating quality, and owning what ships.

Responsibilities

• Translate business problems into clear specifications, constraints, and implementation plans for AI coding agents.
• Direct AI coding agents to design, build, test, and ship applications, workflows, and AI-enabled tools.
• Decompose ambiguous requests into agent-executable work and validate that outputs meet business and technical requirements.
• Run agents in parallel where useful; review, reconcile, and integrate their output.
• Prototype rapidly to gather feedback and inform product direction.
• Maintain quality, reliability, and architectural discipline.
• Design and maintain agent harnesses, including project context, architectural rules, file conventions, allowed dependencies, and review checkpoints.
• Use test-driven practices to constrain agent output and catch drift early.
• Ensure appropriate automated test coverage across unit, integration, end-to-end, and contract tests based on risk.
• Review agent-produced code for correctness, security, maintainability, and architectural fit.
• Enforce patterns that keep codebases maintainable as AI agents contribute to development.
• Build and operate AI-enabled systems.
• Build solutions using design systems, tool use, APIs, function calling, RAG, DAG, MCP, context engineering, harness engineering and multi-agent workflows where appropriate.
• Evaluate models and AI tools across providers based on cost, quality, latency, reliability, security, and fit for purpose.
• Implement evals, monitoring, logging, and guardrails so AI systems are measurable and supportable.
• Design for reliable and repeatable outputs where the business requires consistency.
• Communicate technical concepts, tradeoffs, risks, and recommendations clearly to technical and non-technical stakeholders.
• Partner with architecture, security, and AI governance to align solutions with enterprise standards.

Qualifications

• Strong understanding of Product Management, UI/UX concepts, Business Analysis, Analytics and SDLCs
• 5+ years of professional software engineering experience, including senior-level code review and architectural judgment.
• Demonstrated experience using AI coding agents or AI-assisted development tools to ship production software beyond basic autocomplete or experimentation.
• Ability to decompose business problems into clear technical specifications, implementation plans, tests, and review checkpoints.
• Strong test-driven development and automated testing practices, including using tests to validate AI-generated or agent-produced code.
• Practical experience building or operating LLM-based systems in production, including evaluation, monitoring, and handling non-deterministic behavior.
• Experience working in enterprise environments with security, governance, data-handling, and production support constraints.
• Strong written and verbal communication skills, including the ability to explain technical tradeoffs to technical and non-technical stakeholders.
• Bachelor's degree in Computer Science, Engineering, a related field, or equivalent practical experience.

Preferred Qualifications

• Direct experience building in React, React Native, and AWS.
• Experience with Product Management, UI/UX, Business Analysis, Analytics and SDLCs
• Experience with AWS services relevant to AI workloads, such as Bedrock, Lambda, ECS/Fargate, API Gateway, S3, DynamoDB, RDS, or Step Functions.
• Experience with agent frameworks, RAG, vector databases, embeddings, context engineering, tool use, or function-calling patterns.
• Experience with LLM observability, evaluation, structured outputs, prompt optimization, guardrails, or deterministic-output patterns.
• Experience with advanced testing practices such as contract testing, property-based testing, or mutation testing.
• Experience with Snowflake, Power BI, geospatial, routing, logistics, transportation, or operations-focused software.
• Experience applying AI within governed enterprise environments, including FERPA-relevant or similarly regulated data.
• Relevant AWS, AI, cloud, or data certifications.