Lead AI Architect ( Agentic SDLC )

Orion Innovation • Edison, NJ

12h

About The Position

Orion Innovation is seeking a hands-on applied AI and software engineering professional to design and build agentic workflows that support software development across the SDLC. The role focuses on turning prototypes into reliable systems that engineering teams trust, connecting requirements, architecture, implementation, testing, code review, deployment, and maintenance.

The position combines hands-on engineering, technical architecture, product judgment, and customer-facing leadership. The successful candidate will partner with engineering leaders, developers, and customers to identify valuable opportunities, deliver working solutions, measure impact, and guide adoption from experimentation through production rollout. Success means teams use the workflows to ship better software, with scoped tasks, measurable quality, visible and recoverable failures, and reusable capabilities.

Requirements

• At least 5 years of experience in software engineering, applied AI, developer tooling, platform engineering, or technically intensive solutions engineering, with a strong record of shipping working systems.
• Strong programming skills in Python, TypeScript, or another widely used language, and confidence navigating unfamiliar codebases.
• Deep practical experience with AI coding tools and concrete examples of workflows, integrations, or agents built and used.
• Hands-on experience building LLM applications with tool calling, structured outputs, context retrieval, and iterative execution.
• Strong understanding of software engineering fundamentals, including architecture, Git, automated testing, code review, CI/CD, debugging, and production operations.
• Ability to judge which tasks benefit from agents versus deterministic automation, and where human review is essential.
• A disciplined approach to evaluating generated code and diagnosing failures across models, prompts, context, tools, and execution environments.
• Clear written and verbal communication, including the ability to explain architectural tradeoffs and lead hands-on technical sessions.
• Comfort with ambiguity, fast experimentation, and ownership from problem discovery through delivery and adoption.

Nice To Haves

• Experience building agents that complete multi-step engineering tasks against real repositories.
• Experience developing agent evaluation frameworks, repository context systems, custom tools, MCP integrations, or orchestration infrastructure.
• Experience integrating coding agents into enterprise environments with meaningful security and operational requirements.
• Contributions to developer tools, open-source projects, technical guides, or reference implementations.
• Ability to demonstrate measurable improvements in engineering throughput or quality without shifting hidden work onto reviewers.
• Strong product instinct, including identifying valuable problems, building the smallest useful solution, and improving it based on evidence.

Responsibilities

• Build coding agents that understand repositories, implement features, diagnose defects, write and run tests, address review feedback, and prepare changes for human approval.
• Design coordinated agentic workflows across issue trackers, repositories, developer environments, CI/CD pipelines, documentation, and observability tools, with clear ownership and approval points.
• Engineer reliable agent execution, including context management, tool integrations, orchestration, isolated execution environments, state persistence, retries, and recovery mechanisms.
• Use AI coding tools such as Codex, Cursor, and Claude Code to prototype, implement, debug, and refine systems, and turn effective practices into repeatable team capabilities.
• Build prototypes, reusable integrations, reference architectures, and automations that demonstrate value and support adoption.
• Develop benchmarks and evaluation harnesses to measure task completion, code correctness, regression rates, security findings, latency, cost, and engineering effort.
• Implement scoped permissions, secrets handling, audit trails, sandboxing, and human review controls to make agent autonomy safe and practical.
• Work with engineering teams to understand constraints, run hands-on workshops, and guide adoption from initial experiments through production rollout.
• Translate deployment experience into actionable product requirements, model feedback, technical guides, and reusable engineering patterns.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed