Director - AI/ML

As part of this role, you will:

• End-to-End Solution Building: Lead the full AI lifecyclefrom sourcing, cleaning, and engineering complex, unstructured data to developing highly scalable AI-ML solutions.
• Core Algorithmic Design: Evaluate, select, and build appropriate Machine Learning and Deep Learning algorithms to ensure model accuracy, optimization, and statistical robustness.
• GenAI & LLM Workflows: Apply recent advancements in Large Language Models (LLMs), GenAI, and Agentic AI frameworks to augment traditional analytics.
• Advanced Architecture: Design and optimize Retrieval-Augmented Generation (RAG) systems,
• context engineering frameworks, agentic ai systems, and specific fine-tuning pipelines to enhance product features and automation.Domain Integration: Partner with business stakeholders to translate domain-specific challenges into clear, actionable data science solutions (e.g., Retail/CPG optimization, forecasting, or omni-channel healthcare/pharma orchestration).
• Production-Scale Infrastructure: Architect end-to-end ML production setups, covering robust data ingestion, model training pipelines, and real-time monitoring.
• DS Ops & MLOps Governance: Implement strict governance frameworks and best practices for Data Science Operations (DS Ops). Guide teams on model operationalization, version control, performance tuning, and automated retraining pipelines across major cloud platforms (Azure, AWS, GCP, or Snowflake).
• Team Scaling & Mentorship: Manage, mentor, and foster technical growth across high-performing, cross-functional teams of data scientists, ML engineers, and analysts.
• Executive Advisory: 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:
• Advanced Data Science:
• Deep expertise in Machine Learning, Deep Learning, Python, TensorFlow, PyTorch, and classical statistical Modelling.
• Modern AI Extensions: Demonstrated proficiency in LLMs, RAG architectures, and fine-tuning pipelines.
• Engineering & Scale: Strong hands-on experience with cloud platforms (AWS, Azure, GCP, or Snowflake), data pipelining, and operationalizing models via MLOps.
• Domain Track Record: Prior success in executing production projects within highly analytical sectors such as Retail, CPG, Insurance or Manufacturing is highly desirable.