MLOps Engineer
First seen by Alion on Sep 30, 2026.
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. The role requires hands-on experience with Python, MLOps, Deep Learning, RAG & LLMs, TensorFlow, and Databricks, along with a strong understanding of ML lifecycle management and production environments.
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.
Skills
MLOps, Python, Tensorflow, Databricks, Machine Learning, Generative AI, Deep Learning, RAG, LLM