MLOps Engineer

Location : Bangalore - Whitefield

Experience : 7 - 10 Years

Employment Type : Full-time

Role Overview :

We are looking for an experienced MLOps Engineer with strong expertise in machine learning engineering, cloud-based ML platforms, and AI/ML model deployment. The ideal candidate will be responsible for building scalable ML pipelines, automating model development and deployment workflows, and enabling reliable productionisation of AI/ML and Generative AI solutions.

Tech Stack :

- Python, MLOps, Deep Learning, RAG & LLMs, TensorFlow, Databricks

Key Responsibilities :

- Design, build and maintain scalable MLOps pipelines for model development, training, validation and deployment.

- Develop and automate CI/CD workflows for machine learning models and AI applications.

- Deploy, monitor and maintain ML models in production environments.

- Work closely with Data Scientists, ML Engineers, Data Engineers and application teams to productionise machine learning solutions.

- Develop and optimise machine learning workflows using Python and TensorFlow.

- Build and manage data and ML pipelines using Databricks and related technologies.

- Support the development and deployment of Deep Learning models.

- Work on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) and other Generative AI use cases.

- Design pipelines for data preparation, feature engineering, model training and inference.

- Implement model versioning, experiment tracking, model validation and lifecycle management.

- Establish monitoring mechanisms for model performance, data quality, drift and system health.

- Optimise ML infrastructure for scalability, reliability and cost efficiency.

- Troubleshoot production issues and continuously improve the reliability of ML systems.

- Ensure security, governance and best practices are followed throughout the ML lifecycle.

- Stay updated with emerging technologies and best practices across MLOps, GenAI and AI engineering.

Required Skills :

- 7 - 12 years of overall experience in MLOps / ML Engineering / AI Engineering.

- Strong hands-on programming experience in Python.

- Strong understanding of MLOps principles and ML lifecycle management.

- Hands-on experience with TensorFlow and Deep Learning frameworks.

- Experience working with LLMs and Generative AI.

- Practical understanding of RAG architectures, embeddings, vector search and retrieval pipelines.

- Strong experience with Databricks and ML/data processing workflows.

- Experience building and managing automated ML pipelines.

- Good understanding of model deployment, monitoring and productionisation.

- Experience with CI/CD, version control and automated deployment practices.

- Strong understanding of data pipelines and data engineering concepts.

Good to Have :

- Experience with cloud platforms such as AWS, Azure or GCP.

- Experience with Docker and Kubernetes.

- Knowledge of MLflow or similar experiment/model lifecycle management tools.

- Experience with vector databases and semantic search.

- Exposure to Agentic AI and AI application development.

- Knowledge of infrastructure-as-code and cloud-native architectures.

- Experience implementing responsible AI, security and governance practices.

Key Competencies :

- Strong problem-solving and analytical skills.

- Ability to work effectively with cross-functional teams.

- Strong communication and stakeholder-management skills.

- Ability to translate AI/ML requirements into scalable production solutions.

- Strong ownership and focus on building reliable, production-ready systems.