AI Solutions Engineer

About the role

You're the backbone of delivery. As an AI Solutions Engineer, you'll own complex features end-to-end-from design through production-and produce work that sets the standard for the rest of the team. You'll build intelligent agents, agentic workflows, and Foundation Layer capabilities that multiple solutions depend on. This role sits between Junior Engineers and the Lead. You deliver independently on hard problems, contribute meaningfully to architecture decisions, and make the engineers around you better through code review and informal mentoring. The Lead sets technical direction; you execute it at a high level and push back when something doesn't make sense. We expect you to be self-directed. You'll take ambiguous requirements and turn them into working software. You'll make sound trade-off decisions. You'll know when to build something reusable and when to ship something specific. If you want deep technical ownership without the overhead of formal leadership, this is your role.

Responsibilities

• Design & Deliver
• Own complex features and services end-to-end-design, build, test, deploy, monitor
• Build intelligent agents, conversational interfaces, and agentic workflows
• Develop Foundation Layer services-reusable AI capabilities exposed as REST APIs and MCP servers
• Design and implement integrations between AI capabilities and enterprise systems
• Make sound technical decisions independently, escalating when appropriate
• Troubleshoot complex issues across the full stack-prompts, orchestration, APIs, infrastructure
• Architecture & Technical Quality
• Contribute to architecture decisions and advocate for good design
• Identify opportunities to extract reusable capabilities from solution-specific work
• Write code that others can maintain-clean, well-tested, well-documented
• Drive quality improvements: better test coverage, better patterns, better tooling
• Evaluate new technologies and recommend adoption when they provide clear value
• Team Contribution
• Mentor junior engineers through pairing, code review, and coaching
• Conduct thorough, constructive code reviews that raise team quality
• Contribute to technical interviews and hiring decisions
• Share knowledge through documentation, design discussions, and informal teaching
• Propose and drive improvements to team processes and engineering practices
• Stakeholder Collaboration
• Translate business requirements into technical designs with appropriate trade-offs
• Provide accurate estimates and communicate risks proactively
• Partner with QA and Analysts to ensure quality and completeness
• Represent engineering perspectives in cross-functional discussions

Requirements

• Required
• 3-6 years of software engineering experience with demonstrated ability to deliver complex work independently
• Strong programming fundamentals in Python and/or TypeScript
• Experience designing and building APIs, services, or distributed systems
• Solid understanding of cloud platforms (Azure preferred, AWS/GCP acceptable)
• Track record of writing well-tested, production-quality code
• Experience with code review-both giving feedback that improves code and receiving it constructively
• Ability to take ambiguous requirements and deliver working solutions
• Preferred
• Experience building AI/ML applications, conversational interfaces, or intelligent automation
• Familiarity with LLM concepts: prompt engineering, RAG patterns, function calling, evaluation
• Experience with Microsoft Power Platform (Power Automate, Copilot Studio)
• Background in Azure AI services, OpenAI APIs, or similar LLM platforms
• Understanding of orchestration frameworks (Semantic Kernel, LangChain, or similar)
• Experience in staffing, HR tech, or workforce management
• Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines
• Technology Stack
• Languages: Python, TypeScript
• Platforms: Azure (Container Apps, Functions, AI Services), Microsoft 365
• Agent Development: Copilot Studio, Power Automate, custom orchestration
• Data: REST APIs, Dataverse, SQL, Snowflake
• AI/ML: Large Language Models, prompt engineering, RAG patterns, MCP
• Tools: Git, GitHub, CI/CD pipelines, Docker
• We don't expect expertise in everything. We expect strong fundamentals and the ability to learn quickly.
• What We're NOT Looking For
• Configuration specialists who rely on drag-and-drop tools instead of writing code
• Engineers who deliver "good enough" and move on without considering maintainability
• Senior-by-title engineers who still need close supervision on design decisions
• People who treat AI as magic rather than engineering-we build, test, and iterate
• What Makes You Stand Out
• You write code you'd be comfortable handing to someone else to maintain
• You know when to build for reuse and when to ship something purpose-built
• You push back on bad ideas respectfully, including your own
• You make junior engineers better by working alongside them
• You take ownership of problems, not just tasks
• You've debugged something truly painful and came out the other side with a better system

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