Associate Architect

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.