Associate Director Engineering

About the Company:

About the Role:

Responsibilities:

• Strategy & Leadership:

• Define and own the technical roadmap and architectural vision for the entire MLOps lifecycle, from feature engineering to model serving and monitoring.
• Implement the ML platform strategy, overseeing the architecture of core components like the feature store, model registry, and experiment tracking frameworks.
• Collaborate with Engineering, ML, and Insights teams to foster a data-driven culture across the organization.

• Cross-Functional Collaboration:

• Collaborate closely with Data Scientists to understand their 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 their most complex technical challenges.
• Set and monitor OKRs & KPIs to deliver high-impact, reliable data and ML solutions.

• Operational Excellence:

• Ensure the ML platform's reliability, scalability, and cost-effectiveness.
• Oversee the platform's performance and resource utilization, proactively identifying and resolving bottlenecks.
• Optimize cloud infrastructure (preferably on Azure) for efficiency and performance.

• Innovation:

• Stay ahead of industry trends and the latest advancements in AI/ML, such as LLMs, Generative AI, and new MLOps tools.
• Drive research and development efforts and lead Proof of Concepts (POCs) to introduce new technologies that solve Myntra's unique business problems.

Qualifications:

• A Bachelor's or Master's degree in Computer Science, Engineering, or a 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.
• A minimum of 5+ years of specific, hands-on experience as a Machine Learning Engineer is required.
• 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: Proven, hands-on experience designing and implementing a complete MLOps stack in a production environment.
• Big Data & Feature Engineering: Expertise with distributed data processing technologies like Apache Spark. Experience designing and building feature stores is required.
• Cloud and Distributed Systems: Strong knowledge of a major cloud platform (preferably Microsoft Azure/GCP) and a deep understanding of distributed systems architecture.
• Orchestration & Deployment: Strong understanding of containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for ML models.
• ML Frameworks: In-depth, hands-on knowledge of popular ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Generative AI: Hands-on experience with modern Generative AI technologies, including Large Language Models (LLMs), RAG, and vector databases.
• Technical Leadership: A proven track record of leading engineering teams, defining architectural best practices, and driving impactful technical decisions.