Director - AI/ML

As part of this role, you will: - Lead the full AI lifecycle from sourcing, cleaning, and engineering complex, unstructured data to developing highly scalable AI-ML solutions. - Evaluate, select, and build appropriate Machine Learning and Deep Learning algorithms to ensure model accuracy, optimization, and statistical robustness. - Apply recent advancements in Large Language Models (LLMs), GenAI, and Agentic AI frameworks to augment traditional analytics. - Design and optimize Retrieval-Augmented Generation (RAG) systems, context engineering frameworks, agentic ai systems, and specific fine-tuning pipelines to enhance product features. - Partner with business stakeholders to translate domain-specific challenges into clear, actionable data science solutions. - Architect end-to-end ML production setups, covering robust data ingestion, model training pipelines, and real-time monitoring. - Implement strict governance frameworks and best practices for Data Science Operations (DS Ops) and MLOps Governance. - Manage, mentor, and foster technical growth across high-performing, cross-functional teams of data scientists, ML engineers, and analysts. - Translate highly complex algorithmic outcomes and technical findings into clear, value-driven insights and strategic roadmaps for senior enterprise stakeholders. Who do we expect - 15+ years of total experience in data science, predictive modeling, or advanced analytics, having spent significant time as a hands-on, code-writing practitioner. - 5+ years of proven leadership experience managing, growing, and guiding specialized data science teams on production-level projects. - Exposure to GenAI and Agentic AI. - Masters or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a highly quantitative field (preferred). Core Technical Toolkit: - Deep expertise in Machine Learning, Deep Learning, Python, TensorFlow, PyTorch, and classical statistical Modelling. - Demonstrated proficiency in LLMs, RAG architectures, and fine-tuning pipelines. - Strong hands-on experience with cloud platforms (AWS, Azure, GCP, or Snowflake), data pipelining, and operationalizing models via MLOps. - Prior success in executing production projects within highly analytical sectors such as Retail, CPG, Insurance, or Manufacturing is highly desirable. As part of this role, you will: - Lead the full AI lifecycle from sourcing, cleaning, and engineering complex, unstructured data to developing highly scalable AI-ML solutions. - Evaluate, select, and build appropriate Machine Learning and Deep Learning algorithms to ensure model accuracy, optimization, and statistical robustness. - Apply recent advancements in Large Language Models (LLMs), GenAI, and Agentic AI frameworks to augment traditional analytics. - Design and optimize Retrieval-Augmented Generation (RAG) systems, context engineering frameworks, agentic ai systems, and specific fine-tuning pipelines to enhance product features. - Partner with business stakeholders to translate domain-specific challenges into clear, actionable data science solutions. - Architect end-to-end ML production setups, covering robust data ingestion, model training pipelines, and real-time monitoring. - Implement strict governance frameworks and best practices for Data Science Operations (DS Ops) and MLOps Governance. - Manage, mentor, and foster technical growth across high-performing, cross-functional teams of data scientists, ML engineers, and analysts. - Translate highly complex algorithmic outcomes and technical findings into clear, value-driven insights and strategic roadmaps for senior enterprise stakeholders. Who do we expect - 15+ years of total experience in data science, predictive modeling, or advanced analytics, having spent significant time as a hands-on, code-writing practitioner. - 5+ years of proven leadership experience managing, growing, and guiding specialized data science teams on production-level projects. - Exposure to GenAI and Agentic AI. - Masters or PhD in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a highly quantitative field (preferred). Core Technical Toolkit: - Deep expertise in Machine Learning, Deep Learning, Python, TensorFlow, PyTorch, and classical statistical Modelling. - Demonstrated proficiency in LLMs, RAG architectures, and fine-tuning pipelines. - Strong hands-on experience with cloud platforms (AWS, Azure, GCP, or Snowflake), data pipelining, and operationalizing models via MLOps. - Prior success in executing production projects within highly analytical sectors such as Retail, CPG, Insurance, or Manufacturing is highly desirable.