Build the Platform Behind AI at Myntra

nMyntra is looking for a Technical Lead – ML Platform & MLOps to help build the next generation of infrastructure that powers machine learning across Myntra.

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nYou 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.

nWe 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.

nWhat You Will Own

nBuild Myntra's ML Platform

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- Architect, build and evolve scalable ML Platform and MLOps capabilities for large-scale production workloads.
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- Create self-service infrastructure that allows Data Scientists and ML Engineers to train, experiment, deploy and operate models without managing underlying infrastructure.
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- Design reusable platform abstractions, SDKs, APIs and tooling that dramatically improve ML developer productivity.
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- Build systems for reproducibility, lineage, versioning, governance and lifecycle management across ML workflows.
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- Drive architecture and technology choices for Myntra's ML infrastructure.
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nDistributed Training & GPU Infrastructure

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- Build infrastructure for large-scale distributed training across CPU and GPU clusters.
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- Design and operate multi-node and multi-GPU training environments.
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- Work with distributed computing frameworks such as Ray or equivalent technologies.
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- Build intelligent scheduling, resource isolation and autoscaling capabilities for ML workloads.
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- Improve utilization of expensive GPU infrastructure through scheduling, workload optimization and capacity management.
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- Design fault-tolerant training systems with checkpointing, retries and recovery mechanisms.
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nML Workflow & Training Platform

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- Build a world-class training platform supporting the complete ML development lifecycle.
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- Design scalable orchestration for training, feature engineering, validation and deployment workflows.
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- Build reusable workflow components using technologies such as Airflow, Ray and Kubernetes.
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- Improve scheduling, dependency management, execution isolation and reliability for thousands of ML workloads.
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- Enable experimentation across different compute environments without exposing infrastructure complexity to users.
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nMLOps & Model Lifecycle

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- Build end-to-end ML lifecycle capabilities covering:
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- Experimentation → Training → Validation → Model Registry → Deployment → Monitoring → Retraining
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- Build experiment tracking and model management using MLflow or equivalent technologies.
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- Enable reliable model versioning, approval, rollout and rollback.
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- Build automated model validation and production-readiness workflows.
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- Enable reproducible ML workflows across development, staging and production environments.
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nModel Serving & AI Infrastructure

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- Build highly scalable infrastructure for real-time, batch and asynchronous model inference.
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- Design model-serving platforms running on Kubernetes and GPU infrastructure.
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- Optimize serving systems for latency, throughput, availability and cost.
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- Explore and adopt technologies such as Ray Serve, NVIDIA Triton, vLLM, SGLang or equivalent platforms where appropriate.
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- Enable production deployment of traditional ML, deep-learning and emerging AI/LLM workloads.
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nPlatform Reliability & Observability

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- Treat ML infrastructure as a production-grade distributed platform.
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- Define and drive SLIs, SLOs, availability and reliability standards for ML platform services.
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- Build deep observability across infrastructure, pipelines, training workloads and inference systems.
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- Troubleshoot challenging production issues spanning Kubernetes, GPU workloads, distributed systems, networking, storage and ML pipelines.
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- Drive root-cause analysis and systematically eliminate recurring operational issues.
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- Design for high availability, fault tolerance and graceful recovery.
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nCloud, Kubernetes & Infrastructure

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- Design scalable compute, networking and storage infrastructure for ML workloads.
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- Build and operate ML systems on Kubernetes and cloud platforms.
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- Automate infrastructure using Terraform or equivalent Infrastructure-as-Code technologies.
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- Build secure, isolated and reproducible runtime environments.
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- Drive infrastructure efficiency through autoscaling, workload placement and cost optimization.
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nML Developer Experience

nA major part of this role is making complex ML infrastructure simple for users.

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- Build developer-facing platforms, SDKs, APIs and abstractions.
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- Reduce the time required to move an ML experiment into production.
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- Eliminate repetitive infrastructure work for Data Scientists and ML Engineers.
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- Build standardized templates and paved roads for ML development.
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- Improve debugging, discoverability and observability of ML workloads.
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- Enable teams to focus on models and business problems rather than infrastructure.
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nTechnical Leadership

nAt E3, we expect you to go beyond implementing individual components.

nYou will:

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- Own architecture and technical direction for major ML Platform initiatives.
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- Lead complex system-design discussions and technical reviews.
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- Convert ambiguous problems into scalable platform solutions.
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- Drive engineering excellence across reliability, scalability, performance and maintainability.
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- Mentor engineers and raise the technical bar of the team.
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- Influence architecture across ML, Data, Platform, SRE and Infrastructure teams.
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- Evaluate emerging technologies and make pragmatic build-vs-buy decisions.
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- Take critical systems from concept through architecture, implementation and production adoption.
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nWhat We Are Looking For

nMust Have

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- 6+ years of strong hands-on software/platform engineering experience.
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- Strong programming skills in Python.
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- Deep hands-on experience with Kubernetes, containers and Linux.
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- Experience building or operating large-scale distributed systems or platform infrastructure.
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- Strong understanding of cloud infrastructure including compute, storage and networking.
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- Experience building production-grade CI/CD and automation platforms.
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- Experience with workflow orchestration such as Apache Airflow or equivalent systems.
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- Experience with ML lifecycle tooling such as MLflow or equivalent platforms.
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- Strong understanding of ML training and deployment workflows.
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- Experience with Infrastructure as Code, preferably Terraform.
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- Robust debugging and production troubleshooting skills.
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- Experience building systems with monitoring, logging, metrics and alerting.
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- Strong fundamentals in system design, reliability and distributed computing.
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nStrong Differentiators

nWe would especially love to meet you if you have worked on:

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- Ray or other distributed computing frameworks
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- Distributed or multi-node ML training
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- GPU and multi-GPU infrastructure
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- Kubernetes-based ML platforms
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- ML training platforms used by multiple teams
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- Model serving and inference infrastructure
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- GPU scheduling and utilization optimization
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- Large-scale workflow orchestration
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- ML platform developer experience
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- Infrastructure cost and performance optimization
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- Feature platforms or feature stores
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nGood to Have

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- Databricks, SageMaker, Vertex AI or similar ML platforms
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- Model monitoring, data drift and automated retraining
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- NVIDIA Triton, Ray Serve, vLLM or SGLang
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- LLM training/inference and LLMOps
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- Vector databases and retrieval infrastructure
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- Model governance and lineage
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- OpenTelemetry, Grafana or similar observability ecosystems
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📌 Technical Lead-Machine learning (Bengaluru)
🏢 Myntra
📍 Bengaluru