senior Software Developer, AI Solutions

Job summary

Build and deliver production agentic AI solutions using context, memory, guardrails, tool calling, skills, and orchestration, including systems that enable agents to develop software. Use AI coding tools, guide and validate agent output, evaluate emerging tools, and own delivery while communicating specifications, decisions, and roadblocks.

Job details

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).

What you’ll do

Build and deliver production agentic AI solutions using context, memory, guardrails, tool calling, skills, and orchestration, including systems that enable agents to develop software. Use AI coding tools, guide and validate agent output, evaluate emerging tools, and own delivery while communicating specifications, decisions, and roadblocks.

Requirements

Requires a computer science or computer engineering degree, at least seven years of experience, and at least five years of Python experience, along with strong production software engineering, architecture, and system-design skills. Candidates should use agentic coding tools daily, understand agentic AI building blocks, validate AI-generated output for technical and security risks, and communicate clearly; production AI systems, relevant frameworks, cloud platforms, and CI experience are advantages.

Listed skills

• Clear Communication · Preferred
• Git · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Python
• Software Architecture
• System Design
• Agentic AI
• Agentic Coding Tools
• Context Engineering
• Memory Systems
• Guardrails
• Tool Calling
• Model Context Protocol (MCP)
• Orchestration
• AI Output Validation
• Git
• Clear Communication
• Specification Writing

Job areas

• Software
• Technology
• Engineering

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