$180k-$220k + super + bonus

Most “AI engineer” roles mean wiring an LLM into a chatbot. This one is about the hard part: making agents that take

real, consequential actions

and get them right every time.

Our client is building an agent-driven travel platform. Its agents turn natural-language requests into live bookings and servicing work across supplier APIs, reservation terminals, company travel policies and persistent booking records. When a supplier times out halfway through a booking, the system has to know what actually happened before it tries again.

They’re looking for a senior engineer to own the engineering around the model, working directly with the founder and owning much of the architecture.

What you’ll work on

Agent runtime and harness design: orchestration, checkpointing, cancellation, budgets, concurrency and handling provider failures

Context engineering and persistent memory, including history compaction, prompt-cache efficiency and memory scoped correctly to each user and company

Typed tools and MCP integrations, keeping model intent separate from deterministic validation and execution, with protection against prompt injection

Reliable transactions: idempotency, reconciliation, authoritative readback and approvals tied to the exact action being approved

Subagent delegation and longer-running workflows

Evaluation and observability: replay suites,



correlated traces, and tracking task success, latency and cost per task

The stack What you’ll bring

Solid production Python, including asyncio, typed interfaces and debugging concurrent systems

You’ve shipped stateful, tool-using LLM agents to real users with external integrations

You can explain how your agent framework or custom runtime actually executes, not just how to call it

Solid backend depth: Postgres, transactions, migrations, caching and distributed-system failure modes

You’ve built evals for probabilistic systems and used traces to diagnose failures

Security fundamentals: tenant isolation, least-privilege tool access and handling sensitive data

Bonus:

contributions to agent SDKs, MCP servers or eval frameworks

GDS/NDC or booking/payment workflows

terminal-based or multi-channel (web, messaging, voice) agents

model routing and inference optimisation

Why it’s worth a conversation You’ll have real ownership from day one. In your first 90 days you’ll map the runtime, set a quality, latency and cost baseline, and ship a harness improvement you can measure.

If you’ve built an agent that did meaningful work through tools, and you can talk through how you diagnosed a failure and proved the fix worked, we'd love to hear from you.

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📌 Artificial Intelligence Engineer (Haymarket)
🏢 Talent Forge Group
📍 Haymarket