MLOps Engineer

Key Responsibilities

• Develop and maintain CI / CD pipelines for machine learning models.
• Automate model deployment, monitoring, and scaling processes.
• Implement and manage version control for code, data, and models.
• Ensure data quality, security, and compliance throughout the ML lifecycle.
• Collaborate with data scientists, engineers, and other stakeholders.
• Optimize infrastructure for ML workloads.
• Troubleshoot and resolve issues in production ML systems.
• Implement logging, monitoring, and alerting for ML pipelines.
• Manage and optimize cloud resources for ML / AI workloads.
• Facilitate knowledge sharing and best practices across teams.
• Mentor peer developers on MLOps / AIOps and DevOps.
• Automate workflows for multi-model deployment on servers and embedded systems.
• Develop and manage APIs for serving multiple deep learning models efficiently using Flask, FastAPI, or similar.
• Optimize and convert models for embedded / PC / Android / Server deployment (e.g., TFLite, ONNX, .NEF) with quantization and pruning.

Required Technical Competencies

(ref : hirist.tech)

Skills Required

Airflow, Prometheus, Elk Stack, Kafka, Grafana, Tensorflow, Nosql, Pytorch, Terraform, Docker, Flask, Python, Azure Devops, Aws, Hadoop, Cloudformation, Sql, Jenkins, Git, Gcp, Ansible, Spark, FastAPI, Azure, Kubernetes