Machine Learning Engineer | Fine-tuning, LLM & RAG

About the product

We run an agentic marketplace for car buyers. Instead of forcing people through filter dropdowns, our users talk to an agent: “I need a 7 seater under R$120k that’s fuel-efficient and suitable for a family with two kids.” The agent researches, compares real inventory, explains trade-offs, and takes the person to a car they can actually buy.

Behind that conversation sits a fleet of models — routers, retrievers, rankers, extractors, summarizers, judges - each doing a specific job under a latency and cost budget. This role owns that fleet.

Responsibilities:

• Evaluation — the offline and online eval suites that tell us whether the agent is actually getting better: golden datasets from real traffic, calibrated LLM-as-judge rubrics, and eval as a release gate for every model, prompt, or pipeline change.
• Model strategy — deciding which model serves which step, and building the router that balances quality, latency, and cost. Benchmarking new releases against our own evals rather than vendor claims.
• Fine-tuning and adaptation — SFT, LoRA, and preference tuning on automotive-domain tasks when it genuinely beats better prompting or retrieval, plus the data flywheel that feeds it.
• The deep research pipeline — multi-step retrieval and synthesis across inventory, specs, pricing, reviews, and ownership cost, with grounding and citations we can trust.
• Serving and lightweight MLOps — inference services, embedding and index refresh jobs, versioning, staged rollout and rollback, and the observability to see quality and cost in production.
• Fallback and redundancy — multi-provider fallback chains, circuit breakers, and graceful degradation, so a slow or unavailable model never becomes a broken experience for the buyer.
• Cost-conscious infrastructure — owning cost per conversation and keeping the stack lean.

Required

• 3+ years building and shipping ML or LLM systems in production (not just notebooks or POCs).
• Fine-tuning working experience (LoRA/QLoRA, SFT, DPO) and familiarity with serving stacks like vLLM, TGI, or Ollama.
• Working experience with agent frameworks and tool-calling architectures, and their failure modes.
• Experience with LLM observability tooling (Datadog LLM Observability, etc.).
• Multi-provider LLM operations and resilience patterns at real traffic volume.
• Strong Python; comfortable owning a service end to end, not handing it off.
• Real, hands-on experience evaluating LLM systems — you’ve built an eval set, argued about a rubric, and caught a regression before users did.
• Practical experience with retrieval systems: embeddings, vector stores, hybrid search, reranking.
• Solid engineering fundamentals: APIs, async, testing, CI/CD, containers, cloud.
• Cost-awareness as an instinct. You reach for the cheapest thing that meets the bar.
• Advanced English for conversation with global teams.

Nice to have

• Familiarity with tools and practices such as Unsloth, Hugging Face, MLflow, LangSmith, and/or Weights & Biases.
• Marketplace, e-commerce, recommender, or search ranking background.

WORK MODEL: Hybrid.
HIRING MODEL: CLT.

Benefits