Senior AI​/ML Engineer

As a Senior AI/ML Engineer, you will:

• 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 product ionise 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 (Git Hub Actions, Azure Dev Ops).
• 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, New Relic, 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 Dev Ops 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 Git Hub Actions and Azure Dev Ops.
• Familiarity with data platforms such as Snowflake, Delta Lake, Redis, and Azure Data Lake.

• 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.

• 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 Dev Ops technologies.

Want to discuss this opportunity 函anded detailed? Feel free to reach out.