Join the builders
We hire people who ship AI systems with customers — not slide decks. If you want to build production agents, models and infrastructure that land in the real world, talk to us.
Role overview
You sit with the customer, own the outcome and ship working AI systems into production. FDEs at Turbo AI are the sharp end of the build — discovery, design, implementation and handover, often in weeks rather than quarters.
What you will do
- Embed with customer teams to turn ambiguous business problems into shippable AI products.
- Design and build end-to-end systems: agents, RAG pipelines, evaluation harnesses and the product surface around them.
- Own delivery from first workshop to production cutover, including hardening, observability and documentation.
- Work directly with founders and AI engineers to pull the right platform capabilities into each engagement.
- Leave the customer with something that runs without you — clear runbooks, metrics and a path to iterate.
What you bring
- Strong full-stack engineering instincts — you can own both the product surface and the AI plumbing.
- Comfort working on-site or closely with customers; you communicate clearly under ambiguity.
- Hands-on experience shipping LLM or agent systems (RAG, tool use, evaluation, prompt and context design).
- Proficiency in TypeScript and/or Python; familiarity with modern web stacks (e.g. Next.js) is a plus.
- Bias to ship: you prefer a working system in production over a perfect design doc.
Nice to have
- Prior consulting, solutions engineering or forward-deployed experience.
- Domain depth in one of our engagement areas (ops, content, data platforms, internal tools).
- Experience handing systems over to customer engineering teams.
If you want to build at the customer edge and see your work land in production fast, this is the role.
Apply for this roleRole overview
You design and build the intelligence layer of the products we ship — agents, model integrations, retrieval, evaluation and the product logic that makes them reliable. You care as much about behaviour in production as about clever prompts.
What you will do
- Design agent architectures, tool interfaces and RAG systems for real customer workloads.
- Build evaluation harnesses, regression suites and quality gates so we know when a system is good enough to ship.
- Integrate frontier and open models; choose the right stack for latency, cost and quality trade-offs.
- Partner with FDEs to land capabilities in customer environments and iterate from production signal.
- Contribute reusable patterns back into our internal builder toolkit.
What you bring
- Solid software engineering background with production experience beyond notebooks.
- Deep familiarity with LLM application patterns: prompting, tool calling, RAG, agents and evaluation.
- Strong Python skills; TypeScript experience is welcome.
- Ability to debug non-deterministic systems — traces, evals, failure modes and cost.
- Comfort working in short delivery cycles with clear ownership.
Nice to have
- Experience with vector databases, orchestration frameworks or custom agent runtimes.
- Background in applied ML, information retrieval or multimodal systems.
- Open-source contributions or public writing on LLM systems.
If you want to build AI that customers actually run — not demos — we want to hear from you.
Apply for this roleRole overview
You build the platform that lets our builders ship faster and more safely — model serving, GPU and cluster operations, observability, CI/CD for AI workloads and the internal tools behind every engagement.
What you will do
- Design and operate model serving, batch and realtime inference paths across cloud and customer environments.
- Own containerisation, Kubernetes and infrastructure-as-code for AI workloads.
- Build monitoring, tracing and cost controls for LLM and agent systems in production.
- Partner with AI engineers and FDEs to harden deployments and shorten time-to-production.
- Improve the shared platform: templates, pipelines and developer experience for the build team.
What you bring
- 2+ years operating production systems; ML or LLM serving experience strongly preferred.
- Proficiency in Python and/or Go; comfort with Terraform, Docker and Kubernetes.
- Experience with cloud platforms (AWS, GCP, Alibaba Cloud or similar).
- Familiarity with CI/CD, observability and reliability practices for data or ML systems.
- Clear written communication — you document what you build so others can run it.
Nice to have
- Hands-on with Ray, KubeFlow, Triton, vLLM or similar serving stacks.
- Experience managing GPU fleets or multi-tenant inference.
- Prior work on developer platforms or internal MLOps tooling.
If you like making AI systems reliable at scale so builders can move faster, this is your seat.
Apply for this role