Job Title: MLOps Engineer
Experience: 7 12 Years
Location: Bangalore
Job Summary
We are seeking an experienced
MLOps Engineer with strong expertise in
Data Science and
AWS Cloud to design, deploy, and maintain end-to-end machine learning solutions. The ideal candidate will have hands-on experience in building scalable MLOps pipelines, automating model lifecycle processes, and optimizing ML infrastructure in production environments. This role offers the opportunity to work with cutting-edge technologies, collaborating closely with Data Scientists and Engineers to drive innovation and efficiency in AI-driven projects.
Key Responsibilities
- Design, develop, and maintain scalable MLOps pipelines for automated model training, testing, and deployment.
- Collaborate with Data Scientists to productionize machine learning models and integrate them into business systems.
- Automate data preprocessing, model training, and deployment workflows using modern orchestration tools.
- Implement and manage CI/CD pipelines for ML models using Jenkins, GitHub Actions, or AWS CodePipeline.
- Manage and optimize ML infrastructure on AWS (SageMaker, Lambda, ECS/EKS, EC2, ECR, S3, CloudWatch).
- Monitor model performance, implement retraining pipelines, and ensure reliability and scalability of deployed models.
- Ensure best practices in version control, reproducibility, and governance for ML assets.
- Collaborate with cross-functional teams (Data Engineering, DevOps, and Product) to enhance ML workflows and delivery efficiency.
- Conduct performance optimization and cost management for ML infrastructure.
- Stay updated on emerging technologies in MLOps and AI to continuously improve operational processes.
Required Skills
- Strong programming experience in Python (Pandas, NumPy, Scikit-learn, etc.).
- Hands-on experience with AWS Cloud Services SageMaker, Lambda, ECS/EKS, EC2, ECR, S3, and CloudWatch.
- Proficiency in MLOps frameworks such as MLflow, Kubeflow, Airflow, or similar.
- Strong understanding of machine learning model lifecycle, deployment strategies, and monitoring techniques.
- Experience with Docker and Kubernetes for containerized deployments.
- Practical knowledge of CI/CD pipelines and automation tools (Jenkins, GitHub Actions, AWS CodePipeline).
- Familiarity with data pipelines, feature stores, and Git-based version control systems.
- Strong problem-solving, debugging, and performance optimization skills.
Good To Have
- Experience with Terraform or CloudFormation for infrastructure as code (IaC).
- Exposure to DataOps or Feature Store design.
- Knowledge of model governance, compliance, and auditability in ML workflows.
- Familiarity with PyTorch, TensorFlow, or Hugging Face model deployment.
- Experience in monitoring tools (Prometheus, Grafana, or ELK Stack).
Soft Skills
- Strong analytical and communication skills.
- Ability to work collaboratively across teams in a fast-paced environment.
- Passion for automation, scalability, and production-grade AI solutions.
- Continuous learning mindset to stay updated with the latest in ML and DevOps.
