MLOps Engineer

Position : MLOps EngineerExperience : 3+ YearsLocation : BangaloreEmployment Type : Full-TimeRole Overview :We are looking for an experienced MLOps Engineer with 3+ years of experience to build, automate, deploy, and maintain scalable machine learning workflows and production ML systems. The ideal candidate will work closely with Data Scientists, ML Engineers, Software Engineers, and DevOps teams to operationalize machine learning models and ensure reliable, scalable, and efficient deployments.Key Responsibilities :- Design, develop, and maintain end-to-end MLOps pipelines for model training, validation, deployment, and monitoring.- Build and manage CI/CD pipelines for machine learning models and data workflows.- Automate model training, testing, deployment, versioning, and rollback processes.- Deploy ML models to production using Docker and Kubernetes.- Implement model monitoring, logging, alerting, and performance tracking.- Monitor data and model drift and support automated retraining workflows.- Manage model artifacts, experiment tracking, and model registries.- Collaborate with Data Scientists and ML Engineers to productionize machine learning models.- Develop scalable and reliable ML infrastructure on cloud platforms.- Troubleshoot production ML pipelines and deployment issues.- Follow best practices for security, governance, reproducibility, and version control.- Optimize ML infrastructure for performance, scalability, reliability, and cost.Required Skills :- 3+ years of experience in MLOps, ML Engineering, DevOps, or a related field.- Strong proficiency in Python and scripting.- Hands-on experience with MLOps lifecycle and ML model deployment.- Experience with MLflow, Kubeflow, Airflow, or similar MLOps tools.- Strong knowledge of Docker and Kubernetes.- Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.- Good understanding of Git and version control.- Experience with at least one cloud platform :i. AWSii. Microsoft Azureiii. Google Cloud Platform (GCP)- Knowledge of REST APIs and model serving using FastAPI/Flask.- Understanding of model versioning, experiment tracking, and model registry.- Experience with monitoring tools such as Prometheus and Grafana.- Good understanding of Linux/Unix environments.Good to Have :- Experience with AWS SageMaker, Azure ML, or GCP Vertex AI.- Knowledge of Terraform or Infrastructure as Code (IaC).- Experience with Spark, Kafka, or data pipelines.- Exposure to GenAI/LLMOps, model deployment, or RAG pipelines.- Knowledge of feature stores and automated model retraining.- Experience with cloud-native ML architectures.- Understanding of data drift and model drift detection.Educational Qualification :Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.Preferred Candidate Profile :- Strong problem-solving and troubleshooting skills.- Good understanding of software development and DevOps practices.- Ability to work collaboratively with Data Science, ML, Engineering, and Operations teams.- Strong communication and documentation skills.- Ability to work in a fast-paced environment and manage production-critical ML systems. (ref:hirist.tech)