ML Operations Engineer

Job Summary

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.