Machine Learning Tech Lead, GenAI

Provectus helps companies adopt ML/AI to transform the ways they operate, compete, and drive value. The focus of the company is on building ML Infrastructure to drive end-to-end AI transformations, assisting businesses in adopting the right AI use cases, and scaling their AI initiatives organization-wide in such industries as Healthcare & Life Sciences, Retail & CPG, Media & Entertainment, Manufacturing, and Internet businesses.

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.

-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.

-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.

-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.

-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.

-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.