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Senior Machine Learning Engineer - AI in India, India - Jobeax

Senior Machine Learning Engineer - AI in India, India is listed on Jobeax. Browse 30,000+ vacancies available.

Our partner is looking for a Senior Machine Learning Engineer based in India.

We are looking for an experienced Machine Learning Engineer to help evolve and operate a globally deployed recommender system.

You will play a key role in strengthening the architecture, deployment processes, and operational foundations that support production machine learning.

The position has a strong focus on MLOps, AWS, automation, observability, and building reliable ML systems at scale.

You will work closely with Data Scientists, Data Engineers, Software Engineers, Product Managers, and business stakeholders to turn models into dependable production capabilities.

Beyond hands-on engineering, you will provide technical direction and mentorship while promoting strong software engineering and system design practices.

You will also have the opportunity to evaluate new technologies and introduce improvements across the machine learning lifecycle.

This role is well suited to a senior engineer who enjoys solving complex infrastructure challenges and shaping scalable, production-ready ML platforms.

Drive and continuously improve MLOps practices across the machine learning environment.

Build, optimize, and maintain CI/CD pipelines using GitLab to automate reliable ML delivery.
Productionize, deploy, and maintain machine learning models using AWS, with a strong focus on SageMaker.
Design, build, and maintain scalable machine learning and data pipelines.
Develop and maintain robust Python-based ML and data infrastructure.
Implement monitoring, observability, and operational practices to ensure ML systems remain reliable and performant.
Apply software engineering best practices, including automated testing, documentation, version control, and system design.
Provide technical guidance and mentorship to Data Scientists, Data Engineers, and MLOps Engineers.
Collaborate closely with Product Managers, engineers, data professionals, and business stakeholders to align technical solutions with business objectives.
Evaluate emerging technologies, tools, and methodologies that can improve machine learning capabilities and operational efficiency.
Contribute to the continuous improvement of the ML platform and its ability to support scalable production workloads. 5+ years of professional experience in Machine Learning Engineering or a closely related field.
Strong hands-on experience deploying, operating, and maintaining production machine learning systems.
Expert-level Python skills and strong knowledge of the broader data science and machine learning ecosystem.
Hands-on experience with AWS cloud services, preferably including AWS SageMaker.
Strong understanding of MLOps principles, practices, tooling, and the machine learning lifecycle.
Practical experience with MLflow for experiment tracking and model management.
Experience designing and maintaining GitLab CI/CD pipelines.
Hands-on experience with at least one major deep learning framework, such as PyTorch or TensorFlow.
Proven experience designing and building scalable ML and data pipelines.
Experience implementing monitoring and observability for machine learning systems.
Ability to design, document, explain, and communicate complex technical architectures to both technical and non-technical stakeholders.
Experience mentoring engineers and data scientists and providing technical leadership.
Bachelor's degree in Computer Science, Engineering, or a related technical discipline, or equivalent practical experience.
Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field is a plus.
Experience with Prometheus, Grafana, Evidently AI, or similar monitoring and observability technologies is preferred.
Experience working with large-scale recommender systems is highly valued.
Strong understanding of software engineering principles, architecture, and system design is preferred. B2B contract arrangement.
Opportunity to work on technically challenging machine learning projects with mature engineering practices.
Exposure to modern ML technologies, AWS infrastructure, MLOps tooling, and enterprise-scale systems.
Opportunity to contribute to a globally deployed recommender system and production ML platform.
Collaborative and supportive environment focused on knowledge sharing and professional development.
Exposure to complex machine learning infrastructure, automation, observability, and scalable system design.
Opportunity to evaluate and introduce new technologies that improve ML capabilities and operational efficiency.

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