🛠 Tech Stack

💼 About This Role

You'll own the full ML lifecycle from problem formulation to production monitoring, bridging Data Engineering and Data Science teams. You'll design production-grade ML systems with robust MLOps practices at a data product company working with Fortune 500 clients.

🎯 What You'll Do

• Own end-to-end ML lifecycle: problem framing to deployment and monitoring.
• Design and architect production-grade ML systems with service and data contracts.
• Build ML pipelines using orchestration frameworks like Apache Airflow or Prefect.
• Implement robust MLOps: CI/CD, model versioning, experiment tracking, canary rollouts.

📋 Requirements

• 5–7 years hands-on ML engineering experience with 3 production deployments.
• Production-grade Python with type hints, testing, and modular code.
• Expertise in cloud ML platform (AWS SageMaker, GCP Vertex AI, or Azure ML).
• Expert SQL for complex transformations on cloud data warehouses.

✨ Nice to Have

• LLMOps experience: RAG pipelines, vector databases, fine-tuning.
• Real-time feature engineering with Kafka, Flink, or Spark Streaming.
• Infrastructure as Code (Terraform, Pulumi) for ML infrastructure.

🎁 Benefits & Perks

• 🏖️ Flexible PTO
• 💰 Competitive compensation
• 📈 Growth opportunities
• 🏢 Onsite collaboration in Noida