MLOPS Engineer

Required Skills: Python, Bash scripting, YAML/JSON configuration, Linux systems, CI/CD, GitHub Actions, MLflow, Kubeflow, DVC, Prometheus, Grafana. What Youll Do (Key Responsibilities)Deploy & Manage: Own the deployment of AI/ML models across development, staging, and production environments.Automate Pipelines: Build and maintain seamless CI/CD pipelines for automated ML workflows.Monitor & Optimize: Implement real-time monitoring to track model drift, latency, and inference performance.Collaborate: Partner closely with our Solution Architect and MLOps Lead to standardize and scale our deployment infrastructure.Ensure Reliability: Guarantee model reproducibility, version control, and smooth rollback capabilities.Integrate & Audit: Connect AI services with standard APIs and observability frameworks while maintaining comprehensive deployment logs for audit readiness.CI/CD & IaC: Jenkins, GitLab CI/CD, GitHub Actions, Terraform.ML Lifecycle & Data: MLflow, Kubeflow, DVC.Monitoring & Logging: Prometheus, Grafana, ELK Stack.Cloud Platforms: Experience with AWS SageMaker, Azure ML Studio, or GCP Vertex AI.Governance: Knowledge of traceability and Responsible AI compliance in deployment.Who You Are (Qualifications & Experience)Experience: 36 years of hands-on experience operationalizing AI/ML models with a strong focus on CI/CD automation.Domain Knowledge: Prior exposure to deploying ML pipelines for NLP, computer vision, or speech systems.Education: B.Tech / M.Tech in Computer Science, AI/ML, or a related discipline.Bonus PointsCertifications in DevOps or Cloud Infrastructure (AWS, Azure, or GCP).Published research papers, case studies, or significant open-source contributions. Required Skills: Python, Bash scripting, YAML/JSON configuration, Linux systems, CI/CD, GitHub Actions, MLflow, Kubeflow, DVC, Prometheus, Grafana. What Youll Do (Key Responsibilities)Deploy & Manage: Own the deployment of AI/ML models across development, staging, and production environments.Automate Pipelines: Build and maintain seamless CI/CD pipelines for automated ML workflows.Monitor & Optimize: Implement real-time monitoring to track model drift, latency, and inference performance.Collaborate: Partner closely with our Solution Architect and MLOps Lead to standardize and scale our deployment infrastructure.Ensure Reliability: Guarantee model reproducibility, version control, and smooth rollback capabilities.Integrate & Audit: Connect AI services with standard APIs and observability frameworks while maintaining comprehensive deployment logs for audit readiness.CI/CD & IaC: Jenkins, GitLab CI/CD, GitHub Actions, Terraform.ML Lifecycle & Data: MLflow, Kubeflow, DVC.Monitoring & Logging: Prometheus, Grafana, ELK Stack.Cloud Platforms: Experience with AWS SageMaker, Azure ML Studio, or GCP Vertex AI.Governance: Knowledge of traceability and Responsible AI compliance in deployment.Who You Are (Qualifications & Experience)Experience: 36 years of hands-on experience operationalizing AI/ML models with a strong focus on CI/CD automation.Domain Knowledge: Prior exposure to deploying ML pipelines for NLP, computer vision, or speech systems.Education: B.Tech / M.Tech in Computer Science, AI/ML, or a related discipline.Bonus PointsCertifications in DevOps or Cloud Infrastructure (AWS, Azure, or GCP).Published research papers, case studies, or significant open-source contributions.