Senior AI Quality Engineer

This position requires the eligibility to obtain a security clearance. Non-US citizens may not be eligible to obtain a security clearance. The Defense Industrial Security Clearance Office (DISCO), an agency of the Department of Defense, handles and adjudicates the security clearance process. More information about Security Clearances can be found on the US Department of State government website here: https://www.state.gov/security-clearances 

Accelint is a mission-driven technology company focused on strengthening national security and supporting critical industries. We build the technologies that help operators and organizations see what’s happening, make faster and better decisions, take action with confidence, and stay ready for what comes next. Our work includes advanced sensors, autonomous systems, mission command and control software, AI-enabled training and simulation, and tools that improve logistics, maintenance, and overall readiness.  

For nearly 30 years we have supported military and civilian agencies across the Department of Defense, U.S. allies and partners, and essential industries. When you join Accelint, your work directly contributes to protecting national security and strengthening the systems our society depends on.  

We are looking for a Senior AI Quality Engineer who will focus on ensuring these AI-powered, agentic applications are reliable, observable, and safe to operate in mission-critical environments. You will design and integrate testing guardrails directly into agentic workflows, validate orchestration logic, and build custom evaluation and test harnesses tailored to how these systems actually behave, rather than relying on off-the-shelf QA tools that don't fit the problem space. 

In this role, you will help define how quality is measured, enforced, and continuously evaluated across these systems, from individual agents to end-to-end workflows. You'll work closely with AI engineers, program teams, and infrastructure to embed reliability, security, and evaluation signals into the fabric of the system as it's being built. 

The ideal candidate understands agentic system design, has strong instincts for failure modes in AI-driven workflows, and is comfortable building bespoke testing frameworks, simulators, and evaluation pipelines to ensure these applications can succeed in real operational contexts. 

Role & Responsibilities 

• Hands-on delivery: Participate across the AI solution lifecycle from requirements and design through development, testing, and deployment by writing code, building validation systems, reviewing pull requests, and delivering working AI quality capabilities in an iterative, agile environment. 
• Scenario-driven validation: Build and maintain systems that exercise agentic workflows against realistic scenarios, edge cases, and failure conditions, using custom harnesses and guardrail-style validations, to validate end-to-end behavior and orchestration. 
• Integration hardening: Define contracts, validation, and explicit failure behavior at integration points so agentic workflows degrade safely when dependencies misbehave. 
• Reusable quality components: Create shared libraries, reference harnesses, and validation utilities that teams can reuse to accelerate adoption of AI quality practices. 
• Quality by design: Architect agentic systems so correctness, safety, and expected behavior are enforced by design rather than validated after the fact. 
• Design-time risk reduction: Participate in solution design to identify likely failure patterns in agentic systems and influence architecture and constraints before implementation. 
• AI quality evaluation: Define and implement cost-conscious evaluation strategies for AI-enabled systems, including data quality validation, model relevancy, hallucination detection, failure analysis, and ongoing measurement of AI-driven behavior. 
• Mission-aligned evaluation: Work with cross-functional program teams, includi