ML Operations Engineer

Signify is hiring an ML Operations Engineer for a hybrid, full-time role in Bengaluru. The role focuses on building and supporting scalable Machine Learning and MLOps systems, enabling reliable model deployment through modern architectures such as microservices and containerized environments. The position involves working closely with data scientists and engineers to operationalize ML models, improve system performance, build production-ready ML infrastructure, and integrate intelligent components including multi-agent systems into enterprise workflows. Key Responsibilities: - Design and implement scalable ML infrastructure for model training, deployment, and inference. - Develop and deploy ML solutions as modular, reusable microservices using APIs. - Package ML models and services using Docker. - Deploy and manage services using Kubernetes. - Monitor production systems for model performance, latency, and model drift. - Build and maintain CI/CD pipelines for ML workflows. - Implement versioning, testing, and automated deployment processes. - Develop scalable data pipelines for batch and real-time processing. - Contribute to multi-agent and agent-based AI solutions. - Troubleshoot distributed ML systems and microservices. - Ensure system stability, reliability, and uptime. - Follow secure deployment, API management, and data privacy practices. - Collaborate with engineering, data science, and business teams. - Stay updated on emerging ML, MLOps, distributed systems, GenAI, and agent-based technologies.

Required Skills: - Machine Learning - MLOps - ML Infrastructure - Machine Learning Lifecycle Management - Machine Learning Algorithms - Statistical Modeling - Applied Data Science - Python - SQL - RESTful APIs - Microservices - FastAPI - Flask - Docker - Kubernetes - CI/CD - ML Model Deployment - Model Monitoring - Model Drift - Data Pipelines - Batch Processing - Real-Time Processing - AWS - Azure - Distributed Systems - API Management - Data Privacy - Troubleshooting Preferred / Plus Skills: - AWS SageMaker - Azure ML - Dataiku - Spark - Hadoop - NoSQL Databases - Multi-Agent Systems - LLM-Based Agents - GenAI - Agent-Based Frameworks - Java - Scala