MLOps+DevOps Engineer

Teambees Corp; Pune District; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Teambees Corp is seeking a Senior MLOps + DevOps Engineer to architect, build, and scale on-prem AI platforms. The role requires 8+ years of experience and ownership across ML systems, CI/CD, and production reliability in a restricted environment.

You will design end-to-end ML platform architecture, deploy ML/LLM models on GPU-based on-prem hardware, and optimize performance. This position fosters leadership, collaboration with cross-functional teams, and strong emphasis on automation and

Qualifications

• 8+ years in MLOps / DevOps / Platform Engineering.
• Strong Python and Bash scripting.
• Experience deploying ML/LLM systems in production.
• Linux, Docker, Kubernetes/OpenShift.
• CI/CD tools (Jenkins/GitLab CI).
• SQL and data pipeline experience.
• On-prem or restricted environments experience.

Responsibilities

• 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.
• 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.
• Design and implement CI/CD pipelines for ML models, APIs, and data workflows.
• Enable automated testing, deployment, and release management.
• 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.
• Build pipelines integrating data from structured databases and logs.

Skills

Python

Bash/Shell scripting

ML lifecycle

Production ML systems

Data pipelines

SQL

CI/CD

On-prem environments

Tools

Docker

Kubernetes

OpenShift

Linux

Jenkins

GitLab CI

Terraform

Ansible

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.