Senior MLOps Engineer - CI/CD Implementation

Job Description

Description Senior MLOps Engineer Experience : 6 - 9 Years Location : Chennai, Bangalore, Noida, Hyderabad Role Summary We are looking for a Senior MLOps Engineer with 6-9 years of professional experience to own the end-to-end lifecycle of Machine Learning models in production. You will be the crucial link between Data Science, Data Engineering, and DevOps, applying DevOps principles to the entire ML system to ensure model reliability, scalability, and reproducibility. Key Responsibilities I. Pipeline Design and Automation : End-to-End Pipeline Ownership : Design, implement, and manage automated, scalable ML pipelines for model training, testing, validation, and deployment using tools like Kubeflow, MLflow, or TFX. CI/CD Implementation : Establish and maintain robust CI/CD processes specifically for ML artifacts (data, code, models, and infrastructure) to enable fast, reliable, and repeatable model updates. Infrastructure as Code (IaC) : Utilize tools like Terraform or CloudFormation/ARM to provision and manage ML infrastructure (compute, storage, and networking) on cloud platforms. II. Deployment, Monitoring, And Governance Model Deployment : Develop and manage services for real-time and batch model inference, ensuring high availability and low latency using Docker and Kubernetes (K8s/AKS/EKS/GKE). Monitoring and Alerting : Implement comprehensive monitoring solutions (e.g., Prometheus, Grafana, custom logging) to track model performance (data drift, concept drift, model health) and infrastructure metrics in production. Model Governance : Establish processes for model versioning, lineage tracking, auditing, and rollback capabilities to ensure compliance and reproducibility. III. Collaboration And Optimization Technical Leadership : Act as the MLOps subject matter expert, guiding Data Scientists on model containerization, API development, and best practices for production readiness. Performance Tuning : Optimize the ML platform for cost efficiency, speed, and resource utilization, including GPU/accelerator management and distributed training frameworks. Security : Ensure the security of the ML pipeline, data access, and deployed models in collaboration with security and compliance teams. Required Skills And Qualifications Experience : 6-9 years of experience in software engineering, DevOps, and/or MLOps. Programming Mastery : Expert proficiency in Python (including ML libraries like TensorFlow or PyTorch) and strong experience with Bash/Shell scripting. DevOps and Cloud : Extensive hands-on experience with CI/CD tools (e.g., Jenkins, GitLab CI, Azure DevOps, GitHub Actions) and deep expertise in at least one major cloud platform (AWS, Azure, or GCP). Containerization & Orchestration : Proven experience with Docker and production-level management of container orchestration platforms, primarily Kubernetes. MLOps Tools : Practical experience with key MLOps frameworks/tools such as MLflow, Kubeflow, DVC, or SageMaker/Vertex AI. Data Handling : Solid understanding of data pipelines and data engineering concepts (e.g., ETL, data warehousing, feature stores). Communication : Excellent verbal and written communication skills for cross-functional collaboration. Preferred (Bonus) Skills Experience in Generative AI/LLM deployment (e.g., RAG pipelines, fine-tuning infrastructure). Familiarity with Streaming/Messaging technologies (e.g., Kafka, RabbitMQ) for real-time inference. Experience designing and implementing Feature Stores (e.g., Feast). Relevant Cloud certifications (e.g., AWS ML/DevOps, Azure AI/ML Engineer, GCP Professional ML Engineer). (ref:hirist.tech)