Pleno Machine Learning Engineer
New
J
JobgetherArtificial Intelligence
BrazilFull-TimeMiddle
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLMachine LearningSoftware ArchitectureNLPGenerative AIComputer Vision
Requirements
- Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, Physics, Data Science, or related quantitative fields.
- Proven experience developing and maintaining Machine Learning and Generative AI models in production environments.
- Advanced proficiency in Python, including libraries such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.
- Advanced SQL skills, including query optimization and data modeling.
- Strong knowledge of statistics, including descriptive and inferential analysis and multivariate analysis.
- Experience with Generative AI, including LLM/SLM fine-tuning, prompt engineering, RAG, and frameworks like LangGraph, LangChain, or AutoGen.
- Experience with AI evaluation and observability frameworks such as Ragas, TruLens, DeepEval, or LangSmith.
- Knowledge of document processing technologies, OCR, NLP, and computer vision tools such as Google Document AI, AWS Textract, Azure Form Recognizer, or Tesseract.
- Solid understanding of software architecture, REST APIs, microservices, Git, and scalable engineering practices.
- Ability to independently transform complex and ambiguous problems into technical solutions.
- Strong communication skills for explaining technical concepts to non-technical stakeholders.
Responsibilities
- Design and architect Intelligent Document Processing (IDP) solutions and pipelines for extracting information from complex documents.
- Build and improve machine learning and Generative AI solutions, including fine-tuning strategies, RAG, and computer vision applications.
- Develop and deploy agentic AI workflows using frameworks like LangGraph and n8n to ensure reliable orchestration.
- Create structured data pipelines and analytical models to produce strategic insights.
- Establish evaluation frameworks to monitor model performance, reduce hallucinations, and optimize infrastructure costs.
- Collaborate with Product Managers and Engineering to translate business requirements into AI architectures.
- Support the transition of prototypes through to production implementation.
- Mentor junior team members and share best practices in software development and AI engineering.
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