Full Stack Machine Learning Operations Engineer

Role Overview
We are looking for a Machine Learning Operations Engineer who will own the end-to-end deployment and operation of machine learning systems. This role combines ML deployment, infrastructure, backend engineering, databases, and full-stack web development, including dashboards and internal tools. You will be responsible for taking ML models from handoff to reliable, production-grade systems across edge devices and cloud servers.

Responsibilities

• Deploy, serve, and manage machine learning models for inference on edge devices (ARM-based systems, NVIDIA Jetson, on-prem hardware) and cloud or on-prem servers
• Package ML models into production-ready services using containers and APIs
• Design and develop full-stack web applications, including user-facing and internal dashboards, and alert systems to visualize ML outputs, analytics, system health, and operational workflows
• Design, manage, and optimize databases to store predictions, logs, metadata, and application data
• Implement authentication, authorization, and role-based access across web and backend systems
• Optimize inference performance for latency, throughput, and hardware constraints
• Build, manage, and maintain CI/CD pipelines for ML models and supporting services
• Design, deploy, and operate infrastructure for ML workloads, including container orchestration and cloud resources
• Build and maintain backend services and APIs that expose ML predictions and system functionality
• Set up monitoring, logging, alerting, and automated recovery for ML services and infrastructure
• Manage model versioning, safe rollouts, rollbacks, and OTA updates for edge deployments
• Collaborate closely with ML engineers, data scientists, product, and operations teams to productionize models
• Own ML systems in production end to end, including debugging, incident response, and continuous improvement

Required Skills & Experience

• Strong experience with MLOps and production ML systems
• Hands-on experience deploying ML models on edge devices and servers
• Backend API development experience using Python or similar languages
• Experience with full-stack web development, including frontend frameworks and databases
• Experience with Docker, CI/CD pipelines, and cloud or on-prem infrastructure
• Strong understanding of system reliability, monitoring, and production operations
• Experience in LIDAR Annotation

Good to Have

• Experience with GPU-based inference
• Familiarity with embedded Linux and edge hardware environments
• Experience in manufacturing, IoT, or industrial systems
• Experience with computer vision models

About Biz-Tech Analytics

At Biz-Tech Analytics, we build production-grade computer vision and AI-driven automation solutions for the manufacturing industry. From visual quality control systems to workforce productivity intelligence, we focus on turning complex data into actionable insights through scalable AI infrastructure.