Role Overview :

We are looking for an experienced MLOps / AI Engineer to design, build, deploy, and manage enterprise-scale Machine Learning and Generative AI solutions.

The ideal candidate will have strong expertise in MLOps, cloud platforms, Python development, and production deployment of AI/ML applications.

Key Responsibilities :

- Develop and maintain end-to-end MLOps pipelines for model training, deployment, monitoring, and retraining.

- Build statistical models using Python and Azure Machine Learning services.

- Deploy and manage AI/ML applications using Docker, Kubernetes, and cloud platforms (Azure/AWS/GCP).

- Develop CI/CD pipelines and Infrastructure as Code (Terraform, ARM, Bicep, or CloudFormation).

- Implement LLM-based solutions including RAG, embeddings, vector databases, and prompt orchestration.

- Integrate with Azure OpenAI, OpenAI, Anthropic, or AWS Bedrock.

- Monitor model performance, drift, latency, and system availability.

- Ensure security, governance, compliance, and operational excellence for AI platforms.

Required Skills :

- 8 to 10 years of experience in MLOps, AI Engineering, or Platform Engineering.

- Experience with Azure ML, MLflow, Kubeflow, or SageMaker.

- Strong expertise in Python and Linux.

- Hands-on experience deploying production AI/ML systems.

- Experience with Docker, Kubernetes, CI/CD, and Git.

- Strong knowledge of Azure, AWS, or GCP.

- Experience with Infrastructure as Code (Terraform/ARM/Bicep/CloudFormation).

Preferred Skills :

- Knowledge of LLMs, Generative AI, RAG, and Vector Databases.

- Experience with highly available AI platforms and ITIL processes.

Education Qualification :

Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field.