Senior Software Developer, AI Solutions

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 (Lang Chain, Llama Index, CrewAI) and model evaluation;
Git Lab and Git Lab CI.
Cloud services and deployments (AWS, Azure, GCP).