We're looking for a Software Development Engineer (SDE) who wants to build AI-powered production systems inside strategic enterprise customers — and who thrives in high-ownership environments where delivery speed and customer outcomes matter more than process.

You'll embed directly within a customer's engineering team to independently design, build, deploy, and run AI workstreams end-to-end. Our teams deliver agentic AI solutions — platforms where AI agents and human practitioners collaborate as a unified delivery system — reducing migration timelines from years to months and cutting costs dramatically. You'll own complete delivery from requirements gathering through production deployment, combining the technical rigor of an traditional SDE with the urgency required to ship measurable customer business outcomes.

This is a hands-on builder role. You'll write production-grade code every day, make design trade-offs with appropriate guidance, ship features across the full software lifecycle, and take ownership of workstreams serving Fortune 500 customers. You won't just write code that ships to customers — you'll be on-site watching it work, debugging it in real time, and iterating based on what you learn.

What You'll Build

You'll deliver production AI systems spanning the enterprise cloud migration lifecycle. The work covers several domains:

• Agentic AI — AI agents that autonomously handle migration tasks (discovery, wave planning, runbook generation, infrastructure provisioning) while coordinating with human consultants
• Orchestration and workflow — the coordination layer that enables multiple agents and humans to work in parallel with shared context and minimal overhead
• Platform infrastructure — shared services (project datastores, external system connectors, agent lifecycle management) that underpin the ecosystem
• AWS Transform integration — bidirectional data and workflow connectivity with AWS's flagship enterprise modernization service
• Custom solutions — designing and deploying customer-specific agents and integrations that solve problems unique to their environment

All of it involves building agentic AI systems at production scale with real users, real constraints, and direct accountability to the customer.

Why This Role

• You'll build and deliver, not advise — you own your workstreams end-to-end. You're accountable for working software in production, not slide decks or recommendations.
• AI-native problems from day one — every engagement involves designing agents, orchestrating them, evaluating their outputs, or making them extensible. You'll work at the frontier of applied AI engineering.
• Real ownership with real stakes — you'll own the delivery of complete AI workstreams where the customer is watching. There's nowhere to hide and no shortage of interesting problems.
• Full-stack exposure — React frontends, Python services, AWS CDK infrastructure, AI agent logic. You'll touch it all, and you'll need to.
• Direct customer impact — you won't hear about customer outcomes secondhand. You'll be sitting with the customer when the system you built goes live, and you'll iterate on it together.
• Accelerated growth — you'll develop faster than in a traditional SDE role because you're solving problems end-to-end in high-pressure environments. You'll work alongside senior and principal engineers who will challenge you and expand your technical scope.
• Coaching opportunity — you'll coach customer engineers and new team members on producing high-quality code, raising the bar across every engagement.

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Basic qualifications

• Experience programming with at least one software programming language
• Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations
• Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems
• 3+ years of non-internship professional software development experience

Preferred qualifications

• 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Experience building or deploying AI/ML applications in production environments
• Experience with agentic workflows, RAG pipelines, or foundation model integration
• Experience mentoring junior engineers
• Experience working in customer-facing or consulting-adjacent engineering roles

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