Senior Product Engineer

About the role

Agentic Defense Solutions, Inc. is seeking a Senior Product Engineer to help build the ARIA product family - the operational trust layer for AI in high-consequence environments.

This is an AI-native product engineering role. You will own problems from initial concept through production: understanding the user problem, designing the system, directing AI-assisted implementation, reviewing and validating generated code, and shipping capabilities into real customer environments.

We expect our engineers to use modern AI development tools aggressively. We do not measure engineering contribution by lines of code written by hand. We care about the quality, security, maintainability, and velocity of the systems you ship.

You should have the software engineering depth to build these systems yourself when necessary and, more importantly, to recognize when AI-generated code is wrong, fragile, insecure, over-engineered, or solving the wrong problem.

We are building software at the intersection of AI, distributed systems, data, workflow orchestration, and human decision-making. ARIA enables organizations to put AI into real operational workflows while maintaining control over how AI reasons, what information and tools it can access, when humans must intervene, and how decisions and actions are audited.

That creates interesting engineering problems.

How do you orchestrate long-running agentic workflows that combine models, humans, data, tools, policies, and external systems? How do you represent and reason over complex relationships and mission context? How do you make probabilistic AI systems observable and controllable? How do you build the same platform to operate in modern cloud environments and completely disconnected networks?

You will help us answer those questions.

We're looking for an engineer who enjoys owning difficult problems, moving across traditional engineering boundaries, and shipping products rather than simply completing tickets.

• Design and build core capabilities across the ARIA platform and application ecosystem.
• Own capabilities end-to-end: problem definition, product design, architecture, implementation, testing, deployment, observability, and iteration based on real customer use.
• Build systems that orchestrate AI models, agents, tools, data, policies, human approvals, and external services into reliable operational workflows.
• Develop APIs and services that support complex, long-running, stateful processes.
• Build intuitive user experiences for interacting with AI reasoning, workflows, data, and decisions.
• Design data models capable of representing complex entities, relationships, events, evidence, and operational context.
• Build integrations that allow ARIA to securely interact with customer data sources, applications, models, and infrastructure.
• Help evolve ARIA from an early-stage platform into infrastructure capable of supporting large-scale production deployments.
• Use AI development tools as a core part of how you design, build, test, debug, document, and ship software.
• Effectively direct AI coding agents by decomposing complex engineering problems into clear architectures, specifications, constraints, and implementation plans.
• Review, test, debug, and improve AI-generated implementations rather than treating generated code as inherently correct.
• Use your engineering judgment to identify bad abstractions, security vulnerabilities, architectural problems, unnecessary complexity, performance issues, and subtle correctness failures.
• Develop workflows that allow you to move substantially faster without compromising engineering quality.
• Determine when AI-assisted implementation is appropriate and when a problem requires deeper hands-on engineering.
• Help define how ADS practices AI-native software development as the capabilities of models and development tools continue to evolve.
• Share effective patterns, tooling, and workflows with the broader engineering organization.

We don't care whether you personally typed every line of code. We care whether you understand the systems you ship and are willing to take responsibility for their quality.

• Build and improve agentic workflows involving reasoning, planning, tool use, retrieval, memory, and human-in-the-loop decision-making.
• Integrate commercial and open-source foundation models while maintaining a model-agnostic architecture.
• Develop mechanisms for evaluating, testing, and observing AI behavior in production.
• Build systems for managing context, tools, policies, state, memory, and execution across complex AI workflows.
• Help make AI behavior understandable and auditable by capturing the context, evidence, actions, tool calls, policies, and approvals involved in consequential decisions.
• Experiment with emerging models, frameworks, and techniques and determine where they meaningfully improve the product rather than adopting technology for its own sake.
• Help solve the fundamental engineering challenge of combining probabilistic AI reasoning…