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

1. 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

1. CI/CD & Automation

- Design and implement CI/CD pipelines for ML models, APIs, and data workflows
- Enable automated testing, deployment, and release management

1. 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

1. Data & System Integration





- Build pipelines integrating structured databases and high-volume logs/streaming data
- Support batch and real-time inference architectures

1. 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

1. GenAI & Advanced ML Systems

- Deploy RAG pipelines and vector databases

- Manage LLM serving frameworks
- Work with agent orchestration frameworks

1. 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

Valuable 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.

📌 MLOps+DevOps Engineer (Pune)
🏢 Teambees
📍 Pune