senior Software Developer, AI Solutions

Job Summary

Client is looking for a senior software engineer — a builder first — for our AI Solutions team. This is a hands-on development role, not machine-learning research: you’ll build production agentic solutions for our customers and for Client from the building blocks of agentic systems — context, memory, guardrails, orchestration. You’re past the debate about using AI coding tools — you use them daily to ship more — and you bring the architecture instinct to design systems, guide agents, and validate what they build.

Note: this is a software development role, not ML research or modeling.

Responsibilities:

• Build agentic AI solutions for customers and Client from the core building blocks: context, memory, guardrails, tool calling / MCP, skills, and orchestration.
• Design the harness, context, and loops that let agents do real development — including agents that build software.
• Use agentic coding tools daily to maximize output, then attack the next bottleneck in the SDLC.
• Steer agents with software-architecture direction and validate their output — hallucinated APIs, risky dependencies, security regressions.
• Evaluate and adopt emerging AI tooling into Client’s practices.
• Own delivery end to end and communicate clearly on specs, decisions, and roadblocks.

Qualifications:

• Strong software-engineering foundation with production software you’ve built, shipped, and owned. CS/CE degree, 7+ years’ experience, 5+ in Python.
• Solid architecture and system design — decompose an ambiguous problem, design the solution, guide humans or agents to build it.
• Daily, fluent use of agentic coding tools (Claude Code, Codex, Copilot).
• Fluency with the building blocks of agentic AI: context, memory, guardrails, tool calling / MCP, and skills.
• Judgment to validate AI output — hallucinated APIs, risky dependencies, security regressions.
• Clear communication, precise specification writing, and Git proficiency.

Plus:

• Shipped production agentic systems or AI-powered applications end to end.
• Contributed to MCP servers/tools, eval frameworks, or agent-orchestration frameworks.
• GenAI frameworks (LangChain, LlamaIndex, CrewAI) and model evaluation; GitLab and GitLab CI.
• Cloud services and deployments (AWS, Azure, GCP).