Machine Learning Engineer

UST España & Latam; Manaus; Presencial; BRL 160.000 - 200.000; Tempo integral

Resumo da oferta

A global technology firm in Manaus is seeking a talented Machine Learning Engineer to design, train, and deploy ML models. The ideal candidate has over 3 years of experience in building ML systems and is proficient in Python. Expertise in handling LLMs, familiarity with vector databases, and knowledge of relevant tools is essential. Join a dynamic AI team in making impactful transformations with technology.

Qualificações

• 3+ years building and deploying ML systems.
• Hands-on experience with LLMs/SLMs including fine-tuning and prompt design.
• Experience working with both structured and unstructured data at scale.

Responsabilidades

• Design, train, fine-tune, and deploy ML/LLM models for production.
• Build RAG pipelines using vector databases.
• Collaborate with data engineering to maintain data pipelines for ML workloads.

Conhecimentos

Python

ML system deployment

Fine-tuning LLMs

Prompt engineering

Anomaly detection

Data engineering collaboration

English (B2)

Formação académica

Bachelor’s or Master’s degree in Computer Science or related field

Ferramentas

PyTorch

TensorFlow

Scikit-Learn

Hugging Face Transformers

AWS

GCP

Azure

Descrição da oferta de emprego

We are still looking for talent… and we would love for you to join our team! For over 25 years, UST has worked alongside the world’s best companies to make a real impact through business transformation. Driven by technology, inspired by people, and guided by our purpose, UST supports clients from design to implementation. Together, with more than 30,000 employees in 30 countries, we build to create limitless impact, reaching billions of lives in the process.

About the Role

We’re looking for a talented Machine Learning Engineer to join our growing AI team! As an ML Engineer on our team, you will design, train, fine‑tune, and deploy ML/LLM models that power autonomous exception resolution, anomaly detection, and explainable insights. You’ll work hands‑on with multiple LLM ecosystems like OpenAI GPT, Anthropic Claude, Google Gemini, and Meta LLaMA. You will implement retrieval‑augmented generation (RAG) pipelines, develop prompt engineering and safety techniques, and integrate memory and explainability into agentic workflows.

Key Responsibilities

• Design, train, fine‑tune, and deploy ML/LLM models for production.
• Build RAG pipelines using vector databases and frameworks such as LangChain, LangGraph, and MCP.
• Develop prompt engineering, optimization, and safety techniques for agentic LLM interactions.
• Collaborate with data engineering to maintain data pipelines for ML workloads.
• Conduct feature engineering and embeddings generation on structured and unstructured data.
• Implement model monitoring, drift detection, and retraining pipelines.
• Explore emerging LLM/SLM architectures and multi‑agent orchestration patterns.
• Collaborate cross‑functionally with R&D, data science, product, and engineering teams.
• Mentor junior engineers and contribute to best practices in ML engineering.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related field.
• 3+ years building and deploying ML systems.
• English advance (B2)
• Strong Python skills and experience with PyTorch, TensorFlow, Scikit‑Learn, Hugging Face Transformers.
• Hands‑on experience with LLMs/SLMs including fine‑tuning, prompt design, and inference optimization.
• Familiarity with at least two of the following: OpenAI GPT, Anthropic Claude, Google Gemini, Meta LLaMA.
• Knowledge of vector databases, embeddings, and RAG pipelines.
• Experience working with both structured and unstructured data at scale.
• Understanding of SQL and distributed data frameworks like Spark or Ray.
• Deep knowledge of ML lifecycle: data preparation, training, evaluation, deployment, and monitoring.

Preferred Qualifications

• Experience with agentic frameworks (LangChain, LangGraph, MCP, AutoGen).
• Understanding of AI safety, guardrails, and explainability.
• Hands‑on experience deploying ML/LLM solutions on AWS, GCP, or Azure.
• Familiarity with MLOps practices including CI/CD, monitoring, and observability.
• Background in anomaly detection, fraud/risk modeling, or behavioral analytics.
• Contributions to open‑source AI/ML projects or research publications.

UST is waiting for you!