Associate Architect- ML Platform

Who are we?

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.

Working at Myntra is challenging but fun - we are a young and dynamic team, firm believers in meritocracy, believe in equal opportunity, encourage intellectual curiosity and empower our teams with the right tools, space, and opportunities.

About Myntra:

Myntra is India's largest online fashion and lifestyle store, offering a hassle-free shoppingexperience with the widest range of brands and products. Our cloud-based big data platformprocesses over 10 billion events per day, and we are on a journey to modernize and scaleour data infrastructure. We leverage best-of-breed open-source and Enterprise solutions toprovide self-serve analytics and ML capabilities for Myntra's data consumers.If you are passionate about big data engineering, analytics, and building scalable dataproducts, this is the team for you

The Opportunity

As an Associate Architect in the Machine Learning Platform team, you will be a key technical leader in the Machine Learning Engineering team. You will be responsible for the strategic vision, design, and implementation of Myntra's end-to-end ML platform. This is a high-impact role for a seasoned ML practitioner who has transitioned into an architectural role. You will draw upon your deep, hands-on experience as a Machine Learning Engineer to tackle complex, large-scale challenges and shape the future of how Myntra leverages AI. You will work with a diverse set of ML applications, including computer vision for product tagging, recommendation engines, search ranking, and the new wave of generative AI for content and customer experience.

What You'll Do

• Architectural Leadership: Define and own the technical roadmap and architectural vision for the entire MLOps lifecycle, from feature engineering and data pipelines to model serving and monitoring. Your designs will be informed by a practical understanding of the challenges faced by ML Engineers.
• System Design: Design and build highly scalable, reliable, and cost-effective distributed systems for model training, inference, and real-time serving. This includes core platform components, such as the feature store, model registry, and experiment tracking frameworks.
• Technical Evangelism: Drive the adoption of best practices for MLOps, software development, and system design within the ML Engineering and Data Science teams. Mentor and guide senior ML engineers on their most complex technical challenges.
• Innovation & R&D: Stay abreast of the latest advancements in AI/ML, cloud technologies (e.g., LLMs, Generative AI, MLOps tools), and open-source frameworks. Drive research and development efforts to introduce new technologies that solve Myntra's unique business problems.
• Cross-Functional Partnership: Collaborate closely with Data Scientists to understand their modeling needs and translate them into robust platform capabilities. Your background as a practitioner will allow you to anticipate their pain points and build a truly enabling platform.
• Performance and Cost Management: Oversee the performance and resource utilization of the ML platform. Proactively identify and resolve bottlenecks, and optimize cloud infrastructure (on Azure) to ensure efficiency and cost-effectiveness.

What We're Looking For

• Experience: 8+ years of professional experience in software engineering, with at least 5+ years specifically as a hands-on Machine Learning Engineer . A minimum of 3 years of experience in a lead or architect role focused on building and scaling ML platforms is required.
• Deep MLOps Expertise: Proven, hands-on experience designing and implementing a complete MLOps stack in production. You should have a practitioner's understanding of the full model lifecycle.
• • Data & Feature Engineering: Expertise with distributed data processing technologies like Apache Spark and Kafka . Experience with designing and building feature stores.
• Orchestration & Deployment: Strong understanding of containerization ( Docker ) and orchestration ( Kubernetes ). Experience with CI/CD and deployment pipelines for ML models.
• ML Frameworks: In-depth knowledge and hands-on experience with popular ML frameworks such as PyTorch, TensorFlow, and scikit-learn .
• Cloud and Distributed Systems:
• • Deep expertise with a major cloud provider, preferably Microsoft Azure/GCP .
• Strong understanding of distributed systems architecture, performance, and reliability.
• Technical Leadership: A track record of leading a team of engineers, defining architectural best practices, and driving technical decisions that have a significant impact on an organization.
• Problem-Solving Skills: An ability to think creatively and critically to solve large-scale, open-ended problems, balancing short-term needs with long-term architectural vision.
• E-commerce Domain Knowledge: Experience with AI/ML applications in an e-commerce context (e.g., recommendation systems, computer vision for fashion, search ranking, supply chain optimization) is highly preferred.
• Generative AI: Hands-on experience with modern Generative AI technologies, including Large Language Models (LLMs), fine-tuning, RAG (Retrieval-Augmented Generation), and vector databases.
• Education: A Bachelor's or Master's degree in Computer Science or an ML field.