AI/ML MANAGER @ INTELLYK
The Engineering AI/ML Manager is a hands-on technical leader responsible for delivering AI/ML solutions from design through production while managing a small team of engineers. This role balances people leadership, technical depth, and delivery accountability, making it ideal for a manager who still codes, reviews designs, and actively guides implementation. The role supports enterprise AI initiatives across machine learning, generative AI, LLM-based solutions, and data-driven automation, specifically aligned with healthcare and regulated industry standards.
Key responsibilities include leading the design, development, and deployment of AI/ML solutions in production environments and guiding architecture decisions for ML pipelines. The manager will mentor a team of AI/ML engineers both onshore and offshore, support sprint planning, and foster strong collaboration across engineering, data, and business teams. Additionally, the role involves overseeing the development of Generative AI, RAG pipelines, and promoting MLOps best practices such as CI/CD and model monitoring to ensure compliance with data governance and privacy regulations.
Key responsibilities include leading the design, development, and deployment of AI/ML solutions in production environments and guiding architecture decisions for ML pipelines. The manager will mentor a team of AI/ML engineers both onshore and offshore, support sprint planning, and foster strong collaboration across engineering, data, and business teams. Additionally, the role involves overseeing the development of Generative AI, RAG pipelines, and promoting MLOps best practices such as CI/CD and model monitoring to ensure compliance with data governance and privacy regulations.
Key Requirements
7–10 years of overall engineering experience with 3+ years in AI/ML engineering.
2+ years of people or technical team leadership experience.
Strong hands-on experience with Python and ML frameworks like TensorFlow, PyTorch, or scikit-learn.
Experience with cloud platforms, with a preference for Azure-based AI implementations.
Proven experience in ML model deployment and productionization within enterprise environments.
Experience building and deploying Large Language Model (LLM) or Generative AI solutions.
Solid understanding of data pipelines, APIs, and distributed systems architecture.
Knowledge of MLOps best practices including CI/CD, model versioning, and monitoring.
Ability to lead design, development, and deployment of AI/ML solutions in regulated industries.
Strong communication skills to translate business problems into technical AI/ML solutions.