MLOps+DevOps Engineer

Job Title: Senior MLOps + DevOps Engineer (On-Prem AI Platform)

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

Required Skills:

• Strong Python and scripting (Bash)

• Deep understanding of ML lifecycle and productionization

• Experience deploying ML/LLM systems in production

• Linux, Docker, Kubernetes/OpenShift

• CI/CD tools (Jenkins/GitLab CI)

• SQL and data pipeline experience

Good to Have:

• GPU optimization knowledge

• MLflow / Kubeflow

• Terraform / Ansible

• Experience in on-prem or restricted environments

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.