Lead MLOps DevOps Engineer
First seen by Alion on Sep 18, 2026.
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
Technical Skills :
- Operating Systems & Scripting: Linux, Bash and Python
- Version Control & Source Management: Gitlab, GitHub
- Cloud Platforms: AWS (Amazon Web Services)
- Containerization & Deployment: Docker, Kubernetes
- Infrastructure Automation & Configuration Management: Terraform, Ansible, CloudFormation
- CI/CD Platforms: GitLab CI/CD
- Observability Tools: DataDog or Dynatrace
- MLOps Platforms & Workflow Orchestration: MLflow, Airflow, AWS SageMaker
- Data & API Integration: SQL, REST APIs
- Cloud Security & Access Control: IAM and cloud security controls
Why Join This Opportunity?
- Own the platform layer that turns machine learning models into reliable production services.
- Influence DevOps and MLOps standards across build, deployment, observability, and governance workflows.
- Work at lead level on cloud-native automation and model-serving patterns that improve release speed and operational stability.
Skills
MLOps, Kubernetes, AWS, Python, Terraform, Ansible, Cloud Architect - AI/ML, CI/CD Pipeline, Apache Airflow, DevOps, Machine Learning