Associate Director Engineering

Job Description

About the Company: Myntra is India's leading fashion and lifestyle platform, where technology meets creativity. As pioneers in fashion e-commerce, we've always believed in disrupting the ordinary. We thrive on a shared passion for fashion, a drive to innovate to lead, and an environment that empowers each one of us to pave our own way. We're bold in our thinking, agile in our execution, and collaborative in spirit. Here, we create MAGIC by inspiring vibrant and joyous self-expression and expanding fashion possibilities for India, while staying true to what we believe in. We believe in taking bold bets and changing the fashion landscape of India. We are a company that is constantly evolving into newer and better forms and we look for people who are ready to evolve with us. From our humble beginnings as a customization company in 2007 to being technology and fashion pioneers today, Myntra is going places and we want you to take part in this journey with us.

About the Role: The Associate Director of Machine Learning Platforms at Myntra will spearhead the development and expansion of the company's state-of-the-art, end-to-end ML platform. The vision is to build an integrated platform that enables data scientists and ML engineers to efficiently manage the entire ML lifecycle, accelerating time-to-market and maintaining our leadership in fashion and technology. This role involves building and leading high-performing engineering teams in a dynamic, fast-paced environment. Ultimately, this leader will drive AI-powered growth and operational excellence across the organization.

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.