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Senior machine learning modeler

Pindamonhangaba
beBeeMachine
Modelista
Anunciada dia 17 novembro
Descrição

**Job Description:**

We are seeking a talented Machine Learning Engineer to join our team of innovative thinkers. As an ML Engineer on our team, you will be responsible for designing, training, and deploying machine learning models that drive business growth and improvement.

You will work closely with our data engineering team to maintain data pipelines for machine learning workloads and collaborate with cross-functional teams to ensure seamless integration and deployment of ML solutions.

The ideal candidate will have a strong background in computer science, machine learning, or a related field, with a minimum of 3 years of experience building and deploying ML systems. They should be proficient in Python, PyTorch, TensorFlow, Scikit-Learn, and Hugging Face Transformers, and have hands-on experience with large language models (LLMs) and retrieval-augmented generation (RAG) pipelines.


Key Responsibilities:

* Design, train, and deploy machine learning models for production
* Build RAG pipelines using vector databases and frameworks like 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


Requirements:

* 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


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

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