Machine Learning Engineer

Techrx Recruiting; São Paulo; Presencial; BRL 180.000 - 360.000; Jornada completa

Descripción de la vacante

Techrx Recruiting seeks a senior ML/LLM engineer to own a fleet of models powering an agentic car marketplace. You will develop evaluation suites, optimize model routing, and implement end-to-end pipelines including retrieval, embedding, and governance.

You will work with global teams, tune models with LoRA/QLoRA and SFT, and ensure observability and cost efficiency in production-scale services. São Paulo, Brazil-based role with on-site collaboration.

Formación

• 3+ years building and shipping ML/LLM systems in production.
• Fine-tuning experience with LoRA/QLoRA, SFT, DPO.
• Experience with agent frameworks and tool-calling architectures.
• Experience with LLM observability tooling (Datadog LLM Observability, etc.).
• Multi-provider LLM operations and resilience at real traffic volume.
• Strong Python; end-to-end ownership of services.
• Advanced English for global teams.

Responsabilidades

• Evaluation — offline and online eval suites to measure agent improvements.
• Model strategy — decide which model serves which step and build routing balancing quality, latency, and cost.
• Fine-tuning and adaptation — SFT, LoRA, and preference tuning on automotive-domain tasks.
• The deep research pipeline — multi-step retrieval and synthesis across inventory, specs, pricing, reviews, and ownership cost.
• Serving and lightweight MLOps — inference services, embedding/index refresh, versioning, staged rollout and observability.
• Fallback and redundancy — multi-provider fallback chains and graceful degradation.
• Cost-conscious infrastructure — owning cost per conversation and lean stacks.

Conocimientos

Python

LLM systems

Fine-tuning

LoRA/QLoRA

Agent frameworks

Tool-calling architectures

LLM observability

Multi-provider LLM ops

English proficiency

Retrieval systems

Embeddings/vector stores

Cost-consciousness

Herramientas

vLLM

TGI

Ollama

Datadog LLM Observability

Descripción del empleo

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.
• 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.