Senior AI/ML Engineer (ID:3418)

• Design, develop, deploy, and operateിരുന്ന production-grade machine learning systems across multiple use cases such as recommendations, forecasting, and automation.
• Build and maintain end-to-end ML pipelines covering training, validation, deployment, monitoring, and lifecycle management.
• Focus on ML Ops and ML Platform development, ensuring scalable, reliable, and maintainable ML workflows.
• Collaborate closely with Data Scientists, Engineers, and Product Managers to productionise ML models and research code.
• Develop and expose ML models as scalable APIs and services using tools such as FastAPI.
• Automate model training and deployment using CI/CD pipelines (GitHub Actions, Azure DevOps).
• Improve observability, reliability, and performance of ML systems through logging, monitoring, and alerting.
• Implement model monitoring and drift detection using tools such as Azure Monitor, NewRelic, and custom frameworks.
• Manage and continuously improve ML infrastructure using Terraform, Docker, and container-based platforms.
• Work with orchestration tools such as Airflow or Azure ML to manage ML workflows.

• 6-8 years of experience in ML Engineering, Data Engineering, or DevOps roles Billing exposure to the full ML lifecycle.
• Strong proficiency in Python, with working knowledge of SQL and Bash.
• Hands-on experience with ML frameworks and tools such as MLflow, Scikit-learn, and/or PyTorch.
• Experience building and maintaining ML pipelines and workflows.
• Practical experience with cloud platforms, particularly Azure and AWS.
• Strong understanding of containerisation and orchestration, including Docker and Kubernetes.
• Experience with CI/CD tools such as GitHub Actions and Azure DevOps.
• Familiarity with data platforms such as Snowflake, Delta Lake, Redis, and Azure Data Lake.

You Should Possess the Ability to:

• Translate ML research and prototypes into scalable, production-ready systems.
• Design and operate reliable ML pipelines with strong observability and monitoring.
• Automate deployment and operational workflows to improve speed and reliability.
• Troubleshoot and optimise ML systems for performance and scalability.

What We Bring to the Table:

• Exposure to modern ML Ops tooling and cloud-native architectures.
• Hands-on experience with advanced monitoring, automation, and infrastructure practices.
• Continuous learning through real-world application of ML, cloud, and DevOps technologies.