Role : MLOps Engineer

Experience : 6 to 12 Years

Notice Period : Immediate to 60 Days Preferred

About the Role :

We are looking for a skilled MLOps Engineer to design, implement, and optimize scalable machine learning infrastructure and deployment pipelines.

The ideal candidate will have hands-on experience in automating ML workflows, managing model lifecycle, and building cloud-native MLOps platforms that enable seamless collaboration between Data Science, Data Engineering, and Software Engineering teams.

This role requires expertise in CI/CD for machine learning, containerization, orchestration, cloud platforms, model monitoring, and infrastructure automation to ensure reliable, scalable, and production-ready AI solutions.

Key Responsibilities :

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

- Automate machine learning workflows using CI/CD best practices.

- Develop scalable infrastructure for deploying machine learning models in production.

- Collaborate with Data Scientists and ML Engineers to operationalize AI/ML models.

- Build and manage feature stores, model registries, and experiment tracking systems.

- Implement model versioning, governance, monitoring, and drift detection.

- Develop automated testing, validation, and deployment pipelines for ML models.

- Monitor model performance, infrastructure health, and production environments.

- Optimize model deployment for performance, scalability, and cost efficiency.

- Build infrastructure using Infrastructure as Code (IaC) tools.

- Ensure security, compliance, and governance across AI/ML platforms.

- Troubleshoot production issues and continuously improve platform reliability.

- Collaborate with DevOps, Cloud, and Engineering teams to enhance platform capabilities.

Required Qualifications :

- Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, or a related field.

- 6 to 12 years of overall IT experience with significant hands-on experience in MLOps, DevOps, or ML Platform Engineering.

- Experience deploying machine learning models into production environments.

Preferred Skills :

- Experience working with Generative AI or Large Language Models (LLMs).

- Knowledge of Vector Databases and Retrieval-Augmented Generation (RAG).

- Experience with LangChain, LlamaIndex, or similar AI orchestration frameworks.

- Exposure to NVIDIA GPU infrastructure and distributed training.

- Knowledge of security, compliance, and AI governance frameworks.

- Experience working with enterprise-scale AI platforms.

Preferred Candidate Profile :

- Proven experience designing and managing enterprise MLOps platforms.

- Strong understanding of software engineering and DevOps best practices.

- Experience deploying, scaling, and monitoring machine learning models in production.

- Excellent analytical, troubleshooting, and communication skills.

- Ability to work in cross-functional teams and drive end-to-end ML platform initiatives.

- Candidates serving an immediate to 60-day notice period will be preferred.