Senior MLOps/DevOps Engineer
First seen by Alion on Oct 15, 2026.
Role Overview:
We are looking for a Senior MLOps + DevOps Engineer (8+ years) to architect, build, and scale AI/ML platforms in an on-prem enterprise environment.
This role requires end-to-end ownership of ML systems, infrastructure, CI/CD, and production reliability, enabling scalable deployment of machine learning and GenAI solutions.
Key Responsibilities:
1. Platform Architecture & Ownership:
- Design and own end-to-end ML platform architecture (data - training - deployment - monitoring)
- Define and enforce best practices for scalable and secure ML systems
- Standardize MLOps + DevOps frameworks and processes
2. Model Deployment & Serving:
- Deploy and manage ML/LLM models on GPU-based on-prem infrastructure
- Optimize inference performance (latency, throughput, batching)
- Implement model versioning, A/B testing, and rollback strategies
3. CI/CD & Automation:
- Design and implement CI/CD pipelines for ML models, APIs, and data workflows
- Enable automated testing, deployment, and release management
4. Infrastructure & Containerization:
- Manage Linux-based (RHEL preferred) on-prem infrastructure
- Containerize applications using Docker
- Deploy and orchestrate workloads using Kubernetes / OpenShift
- Operate within restricted or air-gapped environments
5. Data & System Integration:
- Build pipelines integrating structured databases and high-volume logs/streaming data
- Support batch and real-time inference architectures
6. Monitoring, Observability & Reliability:
- Implement end-to-end observability (model + infra)
- Use tools like Prometheus, Grafana, ELK stack
- Ensure high availability, SLA adherence, and incident response
7. GenAI & Advanced ML Systems:
- Deploy RAG pipelines and vector databases
- Manage LLM serving frameworks
- Work with agent orchestration frameworks
8. Leadership & Collaboration:
- Mentor engineers on MLOps and DevOps best practices
- Collaborate with cross-functional teams
- Drive design reviews and production readiness
Tech Stack:
- Python, Bash, Linux, Docker, Kubernetes/OpenShift, Jenkins/GitLab CI, SQL, Prometheus, Grafana, ELK, GPU optimization, MLflow/Kubeflow, Terraform/Ansible
Experience:
- 8+ years in MLOps / DevOps / Platform Engineering
- Proven experience scaling production ML systems
Ideal Candidate:
A hands-on platform architect who can operate across ML systems and infrastructure, driving automation, scalability, and reliability.
Skills
MLOps, DevOps, Python, Kubernetes, Docker, Linux, Generative AI, CI/CD