Machine learning engineer | fine-tuning, llm

Há 2 horas

São Paulo, State of São Paulo, Brasil Netvagas Tempo integral

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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.

Requisitos:

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.

Benefícios

Cartão Flash
Estacionamento
Assistência Médica
Plano Odontológico
Seguro de Vida
Totalpass
Wellhub
Day Off de aniversário
Bônus

Machine learning engineer | fine-tuning, llm

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