Associate Director Engineering

Role Overview: You will be the Associate Director of Machine Learning Platforms at Myntra, responsible for leading the development and expansion of the company's cutting-edge ML platform. Your main objective will be to create an integrated platform that streamlines the ML lifecycle, enabling data scientists and ML engineers to enhance efficiency, accelerate time-to-market, and maintain Myntra's position as a leader in fashion and technology. This role involves building and managing high-performing engineering teams in a dynamic and fast-paced environment, with a focus on driving AI-powered growth and operational excellence throughout the organization. Key Responsibilities: - Strategy & Leadership: - Define and own the technical roadmap and architectural vision for the entire MLOps lifecycle, including feature engineering, model serving, and monitoring. - Implement the ML platform strategy, overseeing core components like the feature store, model registry, and experiment tracking frameworks. - Foster a data-driven culture by collaborating with Engineering, ML, and Insights teams. - Cross-Functional Collaboration: - Work closely with Data Scientists to understand modeling needs and translate them into robust platform capabilities. - Engage with key stakeholders across Product, Marketing, and Operations to drive data governance, quality, and compliance. - Team Leadership: - Build and lead high-performing machine learning engineering teams. - Mentor and guide senior ML engineers on technical challenges. - Set and monitor OKRs & KPIs to deliver high-impact, reliable data and ML solutions. - Operational Excellence: - Ensure the reliability, scalability, and cost-effectiveness of the ML platform. - Monitor platform performance and resource utilization, identifying and resolving bottlenecks. - Optimize cloud infrastructure, preferably on Azure, for efficiency and performance. - Innovation: - Stay updated on industry trends and advancements in AI/ML, such as LLMs, Generative AI, and new MLOps tools. - Lead research and development efforts, conduct Proof of Concepts (POCs), and introduce new technologies to solve business problems. Qualifications: - Bachelors or Masters degree in Computer Science, Engineering, or related ML field. - 14+ years of professional experience in software or data engineering, with at least 5+ years in a senior leadership role managing a large team. - Minimum of 5+ years of hands-on experience as a Machine Learning Engineer. - Proven experience (minimum 3 years) in an architect or lead role focused on building and scaling ML platforms. - Experience in a high-scale e-commerce or tech-driven company is highly preferred. Required Skills: - Deep MLOps Expertise: Designing and implementing a complete MLOps stack in a production environment. - Big Data & Feature Engineering: Expertise with distributed data processing technologies like Apache Spark, and building feature stores. - Cloud and Distributed Systems: Strong knowledge of a major cloud platform (preferably Microsoft Azure/GCP) and distributed systems architecture. - Orchestration & Deployment: Understanding of containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for ML models. - ML Frameworks: Hands-on knowledge of popular ML frameworks such as PyTorch, TensorFlow, and scikit-learn. - Generative AI: Experience with modern Generative AI technologies, including Large Language Models (LLMs), RAG, and vector databases. - Technical Leadership: Track record of leading engineering teams, defining architectural best practices, and making impactful technical decisions.,