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.