Job Description

Title: MLOPS Engineer Experience: 6 to 12 years Job Location: Hyderabad Interview Date: 6th September 2025 at Hyderabad Mode of Interview: In-Person (Scheduled) Note: This is a scheduled interview process. Candidates will be invited based on shortlisting and prior appointment. Kindly note that this is not a walk-in interview. Job Summary We are seeking a skilled and proactive MLOps Engineer to join our AI/ML team. The ideal candidate will be responsible for designing, implementing, and maintaining robust MLOps pipelines and infrastructure across cloud platforms. You will collaborate closely with data scientists, software engineers, and DevOps teams to operationalize machine learning models and ensure scalable, secure, and automated deployment and monitoring. Key Responsibilities Design and implement model deployment, monitoring, and retraining pipelines. Build and maintain CI/CD/CT pipelines for ML workflows using tools like MLFlow, Kubeflow, Airflow, GitHub Actions, and AWS CodePipeline. Develop and manage inference, monitoring, and drift detection pipelines (data drift, model drift). Architect scalable and secure MLOps infrastructure using Kubernetes, AKS, and Terraform. Publish and manage REST APIs for model inference using FastAPI. Track experiments and model performance metrics. Collaborate with cross-functional teams to raise MLOps maturity across the organization. Conduct internal training and presentations on MLOps tools and best practices. Required Skills & Technologies Cloud Platforms: AWS SageMaker, Azure ML Studio, GCP Vertex AI Big Data & Processing: PySpark, Azure Databricks MLOps Tools: MLFlow, Kubeflow, Airflow, GitHub Actions, AWS CodePipeline Infrastructure & Automation: Kubernetes, AKS, Terraform, FastAPI Programming Languages: Python (ML and automation), Bash, Unix CLI Qualifications Bachelor's or Master?s degree in Computer Science, Data Science, or related field. Proven experience in operationalizing ML models using MLOps frameworks. Strong understanding of ML/AI concepts and hands-on experience in model development. Experience with cloud-native development and container orchestration. Familiarity with agile methodologies and DevOps practices. Preferred Qualifications Certification in AWS, Azure, or GCP. Experience with DataRobot, DKube, or similar platforms. Exposure to enterprise-level ML systems and governance.