Senior Full Stack Engineer Id86905

Agile Engine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.

If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you

We are looking for a Senior Full Stack Engineer to deliver production software across. NET, Type Script, and Azure using an agentic-first engineering workflow.

• 4+ years of experience working as a software engineer
• Recent production experience with . NET 8 or 9, C#, ASP. NET Core Web API, Entity Framework Core, and Postgre SQL .
• Recent experience with Type Script, Azure, and real end-to-end delivery with coding agents and rigorous verification . Prompting alone is not enough.
• Strong Azure experience, including production deployment and operational ownership.
• Proven use of Claude Code or equivalent agentic tools for meaningful end to end feature delivery, not only autocomplete or isolated prompting.
• Advanced AI-native engineering capability, including strong judgment about when and how to use AI agents, coding tools, skills, harnesses, MCP integrations, memory/context strategies, and multi-agent workflows. Must be able to select the right approach for the problem, recognize when an agent is failing or over-engineering, and intervene or change strategies appropriately.
• Demonstrated ability to deliver substantial production software primarily through agentic development rather than traditional manual code authoring, while applying senior engineering judgment to architecture, security, testing, verification, and product behavior.
• Ability to independently investigate unclear tickets, identify missing requirements and product edge cases, ask focused questions, and own feature delivery through tested, working completion without step-by-step direction.
• Ability to translate a product objective into executable agent work, coordinate multiple agents when useful, and verify generated output through builds, tests, runtime checks, and product behavior.
• Demonstrated senior product acumen, adaptability to evolving requirements, commitment to functional V1 releases without over-engineering, and sound judgment on balancing self-directed technical investigation with timely stakeholder alignment.
• Experience with multi-tenant Saa S , service boundaries, automated testing, delivery pipelines, and production validation.
• Strong communication, with early escalation of blockers and concise alignment on priorities and tradeoffs.
• Reliable United States Eastern Time overlap for a full time engagement.
• Ability to demonstrate the agentic workflow in a live AI coding exercise and explain tool selection, context management, failure handling, verification, and limitations.
• Upper-intermediate English level.

• Python and Fast API production experience.
• Experience with AI service layers, RAG, vector stores, prompt evaluation, or document generation.
• Healthcare, HIPAA, financial services, or another regulated data environment.
• Azure Service Bus or another asynchronous and event driven messaging platform.
• Terraform, Azure Entra ID, Azure Dev Ops, and enterprise identity patterns.
• Experience with payer or Medicaid integrations, eligibility, prior authorization, or EDI 837 claims.
• Experience with feature governance systems such as Launch Darkly or Post Hog.

• Deliver production features across. NET, C#, React, Type Script, and Azure using an agentic-first engineering workflow.
• Prioritize functional V1 delivery, assigned work, and UAT or demo readiness before optional optimization or tooling work.
• Turn incomplete or changing product direction into a practical delivery plan and keep work moving without repeated resets.
• Align early with stakeholder when requirements, priorities, or product references conflict.
• Use the required AI Skill consistently, and route proposed workflow improvements without displacing committed delivery.
• Bring findings, focused questions, and proposed options— not just report insufficient information.
• Direct AI agents through planning, implementation, testing, debugging, and validation.
• Review and verify generated work rigorously rather than accepting agent output at face value.
• Build and maintain backend services, APIs, and portal experiences across the platform.
• Preserve security, auditability, tenant isolation, encryption, and least privileg