Agentic Ai

Agentic AI Developer

The Role

We're looking for an Agentic AI Developer who lives at the intersection of applied LLM engineering and production software. You'll design and ship the AI agents and orchestration flows at the core of Synthetix: reasoning loops, tool use, multi-agent coordination, memory, and the governance rails that keep them trustworthy.

You're comfortable across the stack, but your center of gravity is agent creation: turning a capability into a reliable, observable, evaluable agent. This is a hands-on builder role. You'll prototype fast, harden what works, and own agents from design through production.

What You'll Do

• Design, build, and ship AI agents using modern agent frameworks (OpenAI Agents SDK, Google's Agent Development Kit (ADK), Microsoft Agent Framework / AutoGen, CrewAI, or equivalents), picking the right tool for each problem.
• Architect multi-agent orchestration and stateful workflows with LangGraph, LangChain, and similar frameworks: planning, routing, hand-offs, retries, and durable long-running execution.
• Enable agents to talk to each other reliably, using agent-to-agent (A2A) communication patterns and protocols so agents can share context, delegate work, and collaborate across systems.
• Implement robust tool / function calling, structured outputs, and external-system integration, including Model Context Protocol (MCP) servers and connectors.
• Build the retrieval and memory layers: RAG pipelines, embeddings, vector search, and short- and long-term agent memory.
• Bake in governance from day one: guardrails, policy enforcement, human-in-the-loop checkpoints, audit trails, and safe-failure behavior. This is the "governed" in governed orchestration.
• Build and own the evals system end to end: test datasets, automated evals that run on every change, LLM-as-judge scoring, and regression tracking, backed by tracing and telemetry (e.g., LangSmith, Langfuse, OpenTelemetry) to measure and continuously improve agent reliability.
• Integrate agents into full-stack applications across APIs, services, and front-end surfaces, collaborating closely with platform, product, and design.
• Optimize for cost, latency, and quality across model providers (OpenAI, Anthropic / Claude, Google / Gemini, and open-weight models).

What You'll Bring

• 3+ years building and shipping production software, with meaningful recent hands-on experience building AI agents or LLM-powered applications.
• Strong Python (the working language of agent development) and solid TypeScript / JavaScript for full-stack integration.
• Hands-on experience with at least one agent framework / SDK (OpenAI Agents SDK, Google ADK, AutoGen, CrewAI, or similar) and one orchestration framework (LangChain / LangGraph or equivalent).
• A solid grasp of LLM building blocks: prompting, function / tool calling, structured outputs, context-window management, RAG, and embeddings / vector databases.
• A good understanding of agent harnesses, the layer around the model that runs the agent loop, executes tools, manages context, and controls permissions.
• Familiarity with Spec-Driven Development (SDD) and AI-driven development methods like BMAD, and how to use them to build with AI coding agents in a structured way.
• Solid understanding of the software development lifecycle (SDLC), from requirements and design through testing, deployment, and maintenance.
• Full-stack fluency: REST / async APIs (FastAPI, NestJS, or similar), relational databases (PostgreSQL), and enough front-end (React) to build and integrate end-to-end.
• Familiarity with cloud (AWS, Azure, or GCP), containers (Docker), and CI/CD.
• A product mindset, strong debugging instincts, and the judgment to make non-deterministic systems behave reliably.
• Strong communication skills, with the ability to explain technical ideas clearly to both technical and non-technical audiences.

Nice to Have

• Experience with agent evaluation and LLM observability tooling (LangSmith, Langfuse, Arize, and similar).
• Experience with MCP, Temporal or other durable-execution engines, and event-driven architectures.
• Exposure to enterprise / regulated environments: SSO, RBAC, data governance, and compliance.
• Insurance domain experience, such as underwriting, claims, or policy servicing etc.
• Fine-tuning, prompt-optimization, or model-routing experience.
• Open-source contributions in the agent / LLM ecosystem.