About The Company
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 The Team
Myntra Data Science team is responsible for developing the core intelligence behind our customer experience, from personalized recommendations to demand forecasting. We build the models that drive significant revenue and enhance user engagement across various touchpoints.
We are building our next-generation ML workloads and model training pipelines leveraging the cloud-native capabilities. This role is key to ensuring our data science modelsranging from classical ML to state-of-the-art GenAIare robustly architected, optimized, and operationalized in target cloud platforms such as GCP and Azure.
The Role: Senior Software Engineer (ML)
As a Senior Software Engineer (ML), you will bridge the gap between Data Science research and production engineering. You will drive engineering excellence by implementing high-performance ML systems that leverage the full power of the cloud.
Your primary focus will be on building and managing end-to-end ML use cases, managing the full model lifecycle, model APIs in python and scaling PySpark jobs. You will work closely with Data Scientists and platform teams to productise their models and ensure seamless CICD deployment on Myntra's ML infrastructure.
Roles and Responsibilities
ML Pipeline Architecture & Engineering
- Scale & Performance: Build and manage complex ML training and inference jobs using Databricks, ensuring high availability, fault tolerance, and data consistency.
- Pipeline Construction: Design and build robust, scalable ML pipelines (ETL, Feature Engineering, Training, Inference) using PySpark, SQL and Databricks
- Cloud-Native API Optimization: Implement model training code optimised for Google Cloud Platform (GCP), leveraging the specific nuances of Google Cloud Storage (GCS) and GKE to maximise throughput, and seamless CI CD.
Model Deployment & Lifecycle Management
- Deployment Strategy: Operationalize ML models by building scalable inference services/APIs on Google Kubernetes Engine (GKE).
- Lifecycle Management: Implement MLOps best practices for model versioning, registry, and monitoring using MLflow (managed within Databricks or GCP). Code review and optimise datascience APIs for best in class practices.
- GenAI Operations: Deploy and manage Small Language Models (LLMs), Vision Language Models (VLMs), and other generative models using tools like Ollama or vLLM/TGI on GPU-accelerated infrastructure.
Optimization & Use Case Building
- Scale Optimization: Optimize PySpark jobs for performance and cost, tuning partitions, memory management, and caching or derived table strategies for massive datasets.
- Use Case Implementation: Partner with Data Scientists to take raw model prototypes and convert them into production-grade systems that solve specific business problems (e.g., Home Page Ranking, Search Ranking, Recommendations, GPU batching).
- Performance Tuning: Monitor and tune model latency and throughput, ensuring our deployments meet strict SLAs.
Desired Skills And Experience
- Experience: 2.5 to 5 years of hands-on experience in Machine Learning Engineering or Data Engineering with an ML focus.
Must Have
- ML Ops: Understanding of Architecture and Design of ML products. Be able to articulate the trade-offs.
- Cloud-Native DevOps: Experience with containerization (Docker) and orchestration (Kubernetes or ECS ) for ML services.
- Data for ML: Breadth of experience working with data blobs, delta lakes, SQL, and storage for ML workflows.
- GCP ML Deployment: Strong hands-on experience deploying models on Google Cloud Platform (Vertex AI or GKE).
- GCP Infrastructure: Familiarity with Kubernetes (GKE) and general GCP infrastructure (IAM, Networking concepts for ML).
- Databricks: Proven experience working with Databricks specifically within the GCP/Azure ecosystem (configuring clusters, Unity Catalog).
- PySpark: Deep understanding of writing, debugging, and optimizing PySpark jobs at scale.
- Core ML Ops: End-to-end experience with Model Training, Model deployment, Lifecycle Management, and Pipeline Orchestration.
Highly desired (Tech Stack Expansion)
- Strong proficiency in SQL for complex data analyses and extraction pipelines/optimisations.
- Experience interacting with noSQL low latency databases such as Aerospike, Redis, MongoDB, ArangoDB.
- Ability to write optimised queries to support and put together A/B analytical dashboards with optimised derived table pipelines and queries
Good to Have
- Azure Context: Familiarity with Azure ML deployment and Databricks on Azure (valuable for understanding our broader ecosystem).
- GenAI Deployment: Hands-on experience deploying LLMs, VLMs, or SLMs on GPUs. Familiarity with serving frameworks like Ollama, TGI, or vLLM.
- Multi-Cloud Experience: Experience working across different cloud providers or transitioning workloads between them.
- Tools & Tech: Familiarity with Kafka, Aerospike, Redis, MongoDB, VectorDBs like Qdrant, Pinecone.
Desired Competencies
- Engineering Rigor: Strong coding standards in Python/Scala with a focus on modularity and testing.
- Collaborative Mindset: Ability to translate Data Science speak into Engineering requirements and vice versa.
- Problem Solving: A knack for debugging complex distributed systems issues (e.g., Spark OOM errors, K8s pod evictions).
- Business Impact: Understanding of how ML models directly affect business metrics and prioritising engineering tasks accordingly.
Required Skills
ml pipelines, orchestration tools, Pypsark, GCP, API
Required Education
B.Tech
