ML Operations Engineer

Signify; Bengaluru; On-site; INR 900,000 - 1,300,000; Full time

Job summary

Signify in Bengaluru is seeking a seasoned ML Engineer to design and operate scalable ML infrastructure, enabling reliable model training, deployment, and inference at scale.

You will build modular ML solutions as microservices, containerized with Docker and orchestrated by Kubernetes, and work closely with data scientists and engineers to productionize models and optimize performance while ensuring security and data privacy.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, or related field.

Responsibilities

• Design scalable ML infrastructure for training, deployment, and inference.
• Develop modular ML solutions as microservices with APIs.
• Package ML models and services with Docker and deploy them using Kubernetes.
• Monitor production systems for model performance, latency, and drift; optimize.
• Build CI/CD pipelines for ML workflows, including versioning and testing.
• Develop scalable data pipelines for batch and real-time processing.
• Contribute to multi-agent or agent-based solutions for complex problems.
• Troubleshoot issues across distributed ML systems; ensure uptime.
• Follow best practices for secure deployment and data privacy.
• Collaborate with engineering, data science, and business teams.
• Stay updated on ML, MLOps, distributed systems, and AI trends.

Skills

Python

SQL

ML concepts

REST APIs

CI/CD for ML

Education

Bachelor’s or Master’s degree in Computer Science or related field

Tools

Docker

Kubernetes

FastAPI/Flask

AWS/Azure

Spark/Hadoop/NoSQL

Job description

About Signify

Through bold discovery and cutting-edge innovation, we lead an industry that is vital for the future of our planet: lighting. Through our leadership in connected lighting and the Internet of Things, we're breaking new ground in data analytics, AI, and smart solutions for homes, offices, cities, and beyond.

About Signify

Through bold discovery and cutting-edge innovation, we lead an industry that is vital for the future of our planet: lighting. Through our leadership in connected lighting and the Internet of Things, we're breaking new ground in data analytics, AI, and smart solutions for homes, offices, cities, and beyond. At Signify, you can shape tomorrow by building on our incredible 125+ year legacy while working toward even bolder sustainability goals. Our culture of continuous learning, creativity, and commitment to diversity and inclusion empowers you to grow your skills and career. Join us, and together, we’ll transform our industry, making a lasting difference for brighter lives and a better world.

Job Summary

More about the role

Builds and supports scalable Machine Learning (ML) and MLOps systems, enabling reliable model deployment through modern architectures such as microservices and containerized environments. Works closely with data scientists and engineers to operationalize models, improve performance, and integrate intelligent components including multi-agent systems into production workflows.

Key Areas of Responsibility

• Contribute to the design and implementation of scalable ML infrastructure supporting model training, deployment, and inference.
• Develop and deploy ML solutions as modular, reusable microservices using APIs, enabling flexible integration with enterprise applications.
• Package ML models and services using Docker and deploy them using orchestration platforms such as Kubernetes to ensure scalability, portability, and reliability.
• Monitor production systems for model performance, latency, and drift; support optimization and improvements.
• Build and maintain CI/CD pipelines for ML workflows, including versioning, testing, and automated deployment.
• Develop and maintain scalable data pipelines for batch and real-time processing to support model training and inference.
• Contribute to the development of multi-agent or agent-based solutions (e.g., orchestration of LLM agents, task-specific services, or workflow agents) to solve complex business problems.
• Troubleshoot issues across distributed ML systems and microservices; ensure system stability and uptime.
• Follow best practices for secure deployment, API management, and data privacy across distributed systems.
• Work with engineering, data science, and business teams to deliver scalable, production-ready solutions.
• Stay updated on emerging technologies in ML, MLOps, distributed systems, and AI (including GenAI and agent-based frameworks).

Critical Experiences

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• Strong understanding of machine learning algorithms, statistical modeling, and applied data science use cases.
• Experience with ML lifecycle management and tools such as AWS SageMaker, Azure ML, Dataiku, or similar platforms.
• Hands‑on experience building RESTful APIs and deploying ML models as microservices using frameworks such as FastAPI, Flask, or similar.
• Working knowledge of Docker for containerization and Kubernetes for service orchestration, scaling, and deployment.
• Exposure to designing or integrating multi-agent systems, LLM‑based agents, or distributed service‑based architectures is a strong plus.
• Experience with data processing frameworks such as Spark, Hadoop, or NoSQL databases.
• Hands‑on experience with AWS or Azure, including deploying and managing scalable ML solutions.
• Proficiency in Python and SQL; familiarity with Java/Scala is a plus.

Everything we’ll do for you

You can grow a lasting career here. We’ll encourage you, support you, and challenge you. We’ll help you learn and progress in a way that’s right for you, with coaching and mentoring along the way. We’ll listen to you too, because we see and value every one of our 27,000+ people. We believe that a diverse and inclusive workplace fosters creativity, innovation, and a full spectrum of bright ideas. With a global workforce present in 70+ countries, we are dedicated to creating an inclusive environment where every voice is heard and valued, helping us all achieve more together. Come join us, and together we can light up the future.