Machine Learning Technical Leader

Há 19 horas

Rio Grande do Sul, Brasil Provectus Tempo inteiro

**Responsibilities**:

- Leadership & Management
- Lead and manage a team of 5-10 engineers, providing mentorship and fostering a collaborative team environment;
- Drive the roadmap for machine learning projects aligned with business goals;
- Coordinate cross-functional efforts with product, data, and engineering teams to ensure seamless delivery.
- Machine Learning & LLM Expertise
- Design, develop, and fine-tune LLMs and other machine learning models to solve business problems;
- Evaluate and implement state-of-the-art LLM techniques for NLP tasks such as text generation, summarization, and entity extraction;
- Expertise in multiple main fields of ML: NLP, Computer Vision, RL, deep learning and classical ML.
- AWS Cloud Expertise
- Architect and manage scalable ML solutions using AWS services (e.g., SageMaker, Lambda, Bedrock, S3, ECS, ECR, etc.);
- Optimize models and data pipelines for performance, scalability, and cost-efficiency in AWS;
- Ensure best practices in security, monitoring, and compliance within the cloud infrastructure.
- Technical Execution
- Oversee the entire ML lifecycle, from research and experimentation to production and maintenance;
- Implement MLOps and LLMOps practices to streamline model deployment and CI/CD workflows;
- Debug, troubleshoot, and optimize production ML models for performance.
- Team Development & Communication
- Conduct regular code reviews and ensure engineering standards are upheld;
- Facilitate professional growth and learning for the team through continuous feedback and guidance;
- Communicate progress, challenges, and solutions to stakeholders and senior leadership.

**Qualifications**:

- Proven experience with LLMs and NLP frameworks (e.g., Hugging Face, OpenAI, or Anthropic models);
- Strong expertise in AWS Cloud Services;
- Strong experience in ML/AI, including at least 2 years in a leadership role;
- Hands-on experience with Python, TensorFlow/PyTorch, and model optimization;
- Familiarity with MLOps tools and best practices;
- Excellent problem-solving and decision-making abilities;
- Strong communication skills and the ability to lead cross-functional teams;
- Passion for mentoring and developing engineers.

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