Machine Learning Engineer

CG/lab; São Paulo; Presencial; BRL 180 000 - 300 000; Tempo integral

Resumo da oferta

CG/lab in São Paulo, Brazil, seeks an experienced ML engineer to design, ship, and maintain production ML/LLM systems. You will work on fine-tuning, agent frameworks, and retrieval pipelines, collaborating with global teams to ensure reliability and observability.

The role emphasizes end-to-end ownership, cost-aware infrastructure, and hands-on evaluation of models at real traffic volume, with a focus on robust, scalable MLOps practices.

Qualificações

• 3+ years building and shipping ML/LLM systems in production.
• Fine-tuning experience (LoRA/QLoRA, SFT, DPO) and familiarity with serving stacks like vLLM, TGI, or Ollama.
• 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.
• Cost-awareness as an instinct. You reach for the cheapest thing that meets the bar.
• Advanced English for conversation with global teams.

Conhecimentos

Python

Production ML systems

Fine-tuning (LoRA/QLoRA/SFT/DPO)

Agent frameworks & tool-calling

LLM observability

Multi-provider LLM operations

English communication

Evaluation rubrics

Retrieval & embeddings

Cost optimization

Ferramentas

vLLM

TGI

Ollama

Datadog LLM Observability

Descrição da oferta de emprego

• 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.

What we’re looking for

Required

• 3+ years building and shipping ML or LLM systems in production (not just notebooks or POCs).
• Fine-tuning experience (LoRA/QLoRA, SFT, DPO) and familiarity with serving stacks like vLLM, TGI, or Ollama.
• 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.
• 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.