Senior MLOps+DevOps Engineer

Teambees Corp; Mumbai, Pune District; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Teambees Corp is seeking a Senior MLOps + DevOps Engineer to architect, build, and scale AI/ML platforms in an on-prem enterprise environment. You will own end-to-end ML systems, infrastructure, CI/CD, and production reliability to enable scalable deployment of ML and GenAI solutions.

The role requires deep expertise in ML lifecycle, on-prem infrastructure, and advanced CI/CD practices. You will lead design reviews, mentor engineers, and collaborate with cross-functional teams to ensure

Qualifications

• 8+ years in MLOps/DevOps/Platform Engineering.
• Experience deploying ML/LLM systems in production.
• Hands-on with on-prem or air-gapped environments.

Responsibilities

• Architect and own end-to-end ML platform design.
• Deploy and manage ML/LLM models on on-prem infra.
• Build CI/CD pipelines for ML models and data workflows.
• Manage Linux-based on-prem infrastructure (RHEL preferred).
• Containerize apps with Docker and orchestrate with Kubernetes/OpenShift.
• Ensure observability, reliability, and incident response.

Skills

Python

Scripting (Bash)

ML lifecycle

Production ML

Linux

Docker/Kubernetes

CI/CD

SQL & Data Pipelines

Tools

Jenkins

GitLab CI

Docker

Kubernetes/OpenShift

OpenShift

Prometheus

Grafana

Job description

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.