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ML Operations Engineer
Lighting
Bengaluru, IND
Full-time
Mid
Onsite
Discovered 3 weeks ago
AWS SageMakerAzure MLDataikuRESTful APIsFastAPIFlask
Free
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AWS SageMakerAzure MLDataiku
Role Overview
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 Skills for This Role
AWS SageMakerAzure MLDataikuRESTful APIsFastAPIFlask
Full Job Posting
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.
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