Lead MLOps DevOps Engineer

What We're Looking For

This lead-level role owns the design and operation of MLOps and DevOps platforms that support reliable model build, test, deployment, and release workflows. The position combines cloud-native infrastructure, container orchestration, automation, and production model serving to improve delivery speed, governance, and operational stability across machine learning environments.

Key Responsibilities

Design and operate scalable MLOps pipelines that support reliable build, test, deployment, and release processes for machine learning workloads.

Lead the implementation of cloud-native infrastructure, container orchestration, and automation practices across development and production environments to improve scalability and repeatability.

Establish monitoring, logging, and alerting standards for model and platform services to improve reliability, performance, and incident response.

Partner with data science, engineering, and security teams to productionize machine learning workloads with governance, repeatability, and compliance.

Define deployment standards for model serving, artifact promotion, and environment parity to reduce release risk and accelerate production adoption.

Drive technical best practices, mentor engineers on platform automation patterns, and continuously improve delivery speed and operational efficiency.

Strengthen secrets handling, access controls, and release governance across CI/CD and runtime environments to improve security and audit readiness.

Must-Have Skills

MLOps & Model Lifecycle: MLOps, Model deployment and serving

Cloud-Native Infrastructure & Orchestration: Containers, Kubernetes

Delivery Automation & Release Engineering: CI/CD pipelines, Artifact and package management

Infrastructure Provisioning & Security: Infrastructure as Code, Secrets management

Monitoring and observability

Python

Good-to-Have Skills

Operating Systems & Scripting: Linux, Bash

Version Control & Source Management: Git, GitHub Actions

Cloud Platforms: Cloud platforms, AWS (Amazon Web Services), Microsoft Azure, Google Cloud Platform

Containerization & Deployment: Docker, Helm, Argo CD

Infrastructure Automation & Configuration Management: Terraform, Ansible

CI/CD Platforms: Jenkins, GitLab CI, Azure DevOps

Cloud Monitoring & Logging: Prometheus, Grafana, ELK stack

MLOps Platforms & Workflow Orchestration: MLflow, Kubeflow, Airflow, Metaflow, AWS SageMaker

Data & API Integration: SQL, REST APIs

Cloud Security & Access Control: IAM and cloud security controls, Service mesh

TensorFlow