Machine Learning Tech Lead

at  Provectus

We are seeking a highly skilled Machine Learning (ML) Tech Lead with a strong background in Large Language Models (LLMs) and AWS Cloud services. The ideal candidate will oversee the development and deployment of cutting-edge AI solutions while managing a team of 5-10 engineers. This leadership role demands hands-on technical expertise, strategic planning, and team management capabilities to deliver innovative products at scale.

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.

• 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;
• Stay ahead of advancements in LLMs and apply emerging technologies;
• 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.