Senior Software Developer, AI Solutions

Job summary

Build and deliver production agentic AI solutions, including the context, memory, guardrails, tool calling, and orchestration that enable agents to perform real development work. 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 Syntax’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, including the context, memory, guardrails, tool calling, and orchestration that enable agents to perform real development work. Use AI coding tools, guide and validate agent output, evaluate emerging tools, and own delivery while communicating specifications, decisions, and roadblocks.

Requirements

Requires a strong software engineering foundation, a CS or CE degree, at least 7 years of experience, and at least 5 years with Python. Candidates should have architecture and system-design skills, daily fluency with agentic coding tools, knowledge of agentic AI building blocks, sound judgment in validating AI output, clear communication, and Git proficiency.

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
• Context Engineering
• Memory Systems
• Guardrails
• Tool Calling
• Model Context Protocol (MCP)
• Agent Orchestration
• Agentic Coding Tools
• Security Validation
• Git
• Clear Communication

Job areas

• Software
• Technology

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