Posted 1w ago
Pay
Salary (undisclosed)
Location
Bangalore, India
On site
Apply by
26 Oct
3 days left
Skills this role screens for
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Still studying?Paid AI and machine learning internships open nowAbout the role
Signify is hiring for the role of ML Operations Engineer!
The work
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Contribute to the design and implementation of scalable ML infrastructure supporting model training, deployment, and inference.
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Develop and deploy ML solutions as modular, reusable microservices using APIs, enabling flexible integration with enterprise applications.
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Package ML models and services using Docker and deploy them using orchestration platforms such as Kubernetes to ensure scalability, portability, and reliability.
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Monitor production systems for model performance, latency, and drift; support optimization and improvements.
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Build and maintain CI/CD pipelines for ML workflows, including versioning, testing, and automated deployment.
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Develop and maintain scalable data pipelines for batch and real-time processing to support model training and inference.
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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.
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Troubleshoot issues across distributed ML systems and microservices; ensure system stability and uptime.
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Follow best practices for secure deployment, API management, and data privacy across distributed systems.
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Work with engineering, data science, and business teams to deliver scalable, production-ready solutions.
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Stay updated on emerging technologies in ML, MLOps, distributed systems, and AI (including GenAI and agent-based frameworks).
Who they want
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Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
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Strong understanding of machine learning algorithms, statistical modeling, and applied data science use cases.
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Experience with ML lifecycle management and tools such as AWS SageMaker, Azure ML, Dataiku, or similar platforms.
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Hands-on experience building RESTful APIs and deploying ML models as microservices using frameworks such as FastAPI, Flask, or similar.
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Working knowledge of Docker for containerization and Kubernetes for service orchestration, scaling, and deployment.
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Exposure to designing or integrating multi-agent systems, LLM-based agents, or distributed service-based architectures is a strong plus.
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Experience with data processing frameworks such as Spark, Hadoop, or NoSQL databases.
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Hands-on experience with AWS or Azure, including deploying and managing scalable ML solutions.
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Proficiency in Python and SQL; familiarity with Java/Scala is a plus.
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