Machine Learning Engineer III View Jobs

Join Swiggy’s Data Science Platform (DSP) team as a builder of the foundational systems that enable our Data Science teams to move from experiment to production. You will work at the intersection of applied ML and platform engineering - partnering with Data Scientists to turn research into reliable, scalable production systems. The role demands hands-on fluency across the full ML stack: modeling (classical, deep learning, and generative AI), productionization, platform infrastructure, and operational excellence., * DS Collaboration & Code Review: Partner with Data Scientists to review notebooks and training scripts, and translate research prototypes into production-grade code.

• Model Engineering & Productionization: Own notebook-to-production for classical ML, deep learning, and generative AI models - covering training pipeline optimization, ONNX export, quantization, and serving integration.
• Platform Engineering: Build and maintain reusable ML platform components - feature stores, model registries, serving infrastructure, experimentation platforms, and model governance frameworks.
• Data Pipelines & Scalable Infrastructure: Build and optimize batch and real-time ML pipelines on Databricks and Snowflake, with end-to-end automation for training, validation, and deployment.
• MLOps, Observability & Opex: Run CI/CD for ML models, own data drift and model quality monitoring, and take full opex responsibility - cost, incidents, capacity, and SLAs.
• Generative AI & LLM Integration: Integrate LLMs, embeddings, and RAG pipelines into the platform, and manage LLM serving infrastructure for cost, rate limiting, and latency at scale.

Do you have experience in gRPC?, * ML & AI Depth: Strong grasp of ML fundamentals (supervised/unsupervised learning, regularization, validation) across model families - tree-based, neural networks, transformers, and embeddings. Familiarity with GenAI architectures and best practices in training and inference. Exposure to LLMs, fine-tuning, or prompt engineering is a plus.

• Engineering Excellence: Expert Python skills with a track record of writing clean, reproducible, production-grade code. Proficiency in TensorFlow or PyTorch for training and serving. Familiarity with containerization (Kubernetes/Docker) and cloud-native ML services.
• Platform & Data Systems: Hands-on with Databricks (jobs, Delta tables, workflows, cluster sizing and cost trade-offs) and Snowflake. Proven experience building ETL pipelines using Spark (PySpark/Scala), Hive, or Presto. Working knowledge of stream processing (Kafka/Flink), feature stores, and experiment tracking tools (MLflow, Weights & Biases, or similar).
• Production, Serving & Scale: Experience deploying and operating models in production via REST/gRPC serving, with a clear understanding of latency budgets and SLA management. Ability to diagnose and resolve performance bottlenecks - covering quantization, batching, caching, async inference, and horizontal scaling.
• MLOps & Reliability: Practical experience with data drift detection, model observability, and experimentation platforms (XP/A/B testing). Versed in model governance, lifecycle management, and feature reusability. Treats the ML platform as a product - owns cost optimization, incident response, and capacity planning, not just deployment.
• DS Partnership, Domain & Experience: 5+ years of experience, with at least 3 in ML Engineering, MLOps, or applied Data Science. Proven ability to work alongside Data Scientists - reviewing modeling decisions and co-owning model quality end-to-end. Familiarity with at least one applied ML domain (recommendations, search, pricing, demand forecasting, or operations research) is strongly preferred.

Bonus Points If You Have:

• Experience with LLM orchestration frameworks (LangChain, LlamaIndex) or fine-tuning open-source models.
• Familiarity with vector search systems (Pinecone, Weaviate, pgvector, or similar).
• Contributions to internal ML platforms, developer tooling, or open-source ML/AI projects.