Senior Machine Learning Operations Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Operations Engineer based in India. You will play a key role in building and scaling the production backbone for advanced machine learning systems that power AI-driven products at enterprise scale. This role sits at the intersection of machine learning, software engineering, and cloud infrastructure, where your focus will be on making ML models reliable, observable, and production-ready. You will design and maintain end-to-end MLOps pipelines that automate training, deployment, monitoring, and retraining workflows. The environment is highly collaborative, working closely with data scientists, software engineers, and product teams to bridge experimentation and production. You will help ensure ML systems remain performant, secure, and cost-efficient as they scale. This is a high-impact role where your work directly enables the deployment of next-generation AI capabilities.

Accountabilities

• Design, build, and maintain end-to-end MLOps pipelines, including CI/CD/CT workflows for training, validation, deployment, and retraining of machine learning models.
• Deploy, monitor, and optimize ML models in scalable production environments using cloud infrastructure such as Amazon Web Services, Google Cloud, or Microsoft Azure.
• Build and maintain infrastructure using Infrastructure-as-Code tools such as Terraform, ensuring reproducibility and scalability of ML systems.
• Develop and optimize data and ML pipelines using tools and frameworks such as Docker, serverless services, and distributed compute systems.
• Implement model governance practices including versioning, lineage tracking, auditing, and compliance enforcement across ML lifecycle stages.
• Continuously monitor model performance, detect drift and degradation, and trigger retraining or remediation workflows as needed.
• Collaborate closely with data scientists and software engineers to transition models from experimentation to production environments.
• Support integration of ML systems into production applications via REST APIs and scalable service architectures.
• Provide architectural guidance and technical mentorship to engineering teams working on ML infrastructure and data systems.
• Document system designs, workflows, and ML architectures to ensure clarity, reproducibility, and knowledge sharing.

Requirements

• 5+ years of experience building, deploying, and scaling machine learning systems in cloud environments such as AWS, GCP, or Azure.
• 7+ years of programming experience in languages commonly used in ML engineering such as Python or Scala.
• Strong hands-on experience with machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, or Hugging Face Transformers.
• Proven experience designing and operating MLOps pipelines, including CI/CD systems for machine learning workflows.
• Strong understanding of applied machine learning concepts including supervised/unsupervised learning, deep learning, and model evaluation techniques.
• Experience working with Generative AI tools and frameworks (e.g., LLM APIs, prompt engineering, or agent-based systems).
• Experience developing and maintaining REST APIs for model serving and integration.
• Strong knowledge of cloud-native architecture, distributed systems, and scalable data pipelines.
• Excellent problem-solving skills and ability to operate in fast-paced, production-critical environments.
• Bachelor’s degree in Computer Science, Engineering, or a related technical field (or equivalent experience).

Benefits

• Competitive compensation package aligned with experience and industry benchmarks.
• Flexible and remote-friendly working environment.
• Opportunity to work on cutting-edge AI and machine learning systems at scale.
• Strong learning culture with exposure to advanced GenAI and MLOps practices.
• Access to modern cloud infrastructure and engineering tooling.
• Health and wellness benefits depending on employment structure.
• Collaborative, high-impact environment working alongside experienced AI and engineering teams.

How Jobgether Works We use an

AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best! Why Apply Through Jobgether?

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