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 setting.

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.

📌 TeamBees - Senior MLOps/DevOps Engineer - AI Platform (India)
🏢 Teambees
📍 India