Technical Lead-Machine learning

Build the Platform Behind AI at Myntra Myntra is looking for a Technical Lead – ML Platform & MLOps to help build the next generation of infrastructure that powers machine learning across Myntra. You will have the opportunity to design and build foundational ML platform capabilities used across multiple ML teams, influence platform architecture, solve large-scale infrastructure challenges, and establish engineering standards for how ML workloads are built and operated at Myntra. We are looking for a strong hands-on engineer who enjoys building platforms, debugging complex distributed systems, and simplifying infrastructure for hundreds of ML and engineering users. What You Will Own Build Myntra's ML Platform • Architect, build and evolve scalable ML Platform and MLOps capabilities for large-scale production workloads. • Create self-service infrastructure that allows Data Scientists and ML Engineers to train, experiment, deploy and operate models without managing underlying infrastructure. • Design reusable platform abstractions, SDKs, APIs and tooling that dramatically improve ML developer productivity. • Build systems for reproducibility, lineage, versioning, governance and lifecycle management across ML workflows. • Drive architecture and technology choices for Myntra's ML infrastructure. Distributed Training & GPU Infrastructure • Build infrastructure for large-scale distributed training across CPU and GPU clusters. • Design and operate multi-node and multi-GPU training environments. • Work with distributed computing frameworks such as Ray or equivalent technologies. • Build intelligent scheduling, resource isolation and autoscaling capabilities for ML workloads. • Improve utilization of expensive GPU infrastructure through scheduling, workload optimization and capacity management. • Design fault-tolerant training systems with checkpointing, retries and recovery mechanisms. ML Workflow & Training Platform • Build a world-class training platform supporting the complete ML development lifecycle. • Design scalable orchestration for training, feature engineering, validation and deployment workflows. • Build reusable workflow components using technologies such as Airflow, Ray and Kubernetes. • Improve scheduling, dependency management, execution isolation and reliability for thousands of ML workloads. • Enable experimentation across different compute environments without exposing infrastructure complexity to users. MLOps & Model Lifecycle • Build end-to-end ML lifecycle capabilities covering: • Experimentation → Training → Validation → Model Registry → Deployment → Monitoring → Retraining • Build experiment tracking and model management using MLflow or equivalent technologies. • Enable reliable model versioning, approval, rollout and rollback. • Build automated model validation and production-readiness workflows. • Enable reproducible ML workflows across development, staging and production environments. Model Serving & AI Infrastructure • Build highly scalable infrastructure for real-time, batch and asynchronous model inference. • Design model-serving platforms running on Kubernetes and GPU infrastructure. • Optimize serving systems for latency, throughput, availability and cost. • Explore and adopt technologies such as Ray Serve, NVIDIA Triton, vLLM, SGLang or equivalent platforms where appropriate. • Enable production deployment of traditional ML, deep-learning and emerging AI/LLM workloads. Platform Reliability & Observability • Treat ML infrastructure as a production-grade distributed platform. • Define and drive SLIs, SLOs, availability and reliability standards for ML platform services. • Build deep observability across infrastructure, pipelines, training workloads and inference systems. • Troubleshoot challenging production issues spanning Kubernetes, GPU workloads, distributed systems, networking, storage and ML pipelines. • Drive root-cause analysis and systematically eliminate recurring operational issues. • Design for high availability, fault tolerance and graceful recovery. Cloud, Kubernetes & Infrastructure • Design scalable compute, networking and storage infrastructure for ML workloads. • Build and operate ML systems on Kubernetes and cloud platforms. • Automate infrastructure using Terraform or equivalent Infrastructure-as-Code technologies. • Build secure, isolated and reproducible runtime environments. • Drive infrastructure efficiency through autoscaling, workload placement and cost optimization. ML Developer Experience A major part of this role is making complex ML infrastructure simple for users. You will: • Build developer-facing platforms, SDKs, APIs and abstractions. • Reduce the time required to move an ML experiment into production. • Eliminate repetitive infrastructure work for Data Scientists and ML Engineers. • Build standardized templates and paved roads for ML development. • Improve debugging, discoverability and observability of ML workloads. • Enable teams to focus on models and business problems rather than infrastructure. Technical Leadership At E3, we expect you to go beyond implementing individual components. You will: • Own architecture and technical direction for major ML Platform initiatives. • Lead complex system-design discussions and technical reviews. • Convert ambiguous problems into scalable platform solutions. • Drive engineering excellence across reliability, scalability, performance and maintainability. • Mentor engineers and raise the technical bar of the team. • Influence architecture across ML, Data, Platform, SRE and Infrastructure teams. • Evaluate emerging technologies and make pragmatic build-vs-buy decisions. • Take critical systems from concept through architecture, implementation and production adoption.