Senior MLOps & DevOps Engineer

Bellcom Technologies; Bhopal; On-site; INR 3,500,000 - 5,500,000; Full time

Job summary

Bellcom Technologies in India, with location in Bhopal, seeks a Senior MLOps & DevOps Engineer to own and operate the end-to-end MLOps platform on secure on-premise infrastructure. The role requires hands-on production GPU experience and the ability to stand up the full stack from bare metal without internet access.

You will deploy LLM inference on local GPUs, manage Kubernetes on bare metal, implement CI/CD with IaC, and maintain air-gapped registries and monitoring dashboards.

Qualifications

• 6–10 years in DevOps/MLOps with GPU infra experience.
• Hands-on NVIDIA GPU ops (H100/A100, L40S).
• Proficiency with ML platforms (MLflow, Kubeflow, Airflow).
• Ability to operate in air-gapped, classified environments.

Responsibilities

• Design and operate the full ML pipeline: ingestion, training, validation, deployment, monitoring, drift detection, and retraining.
• Build and maintain ML pipelines with MLflow, Kubeflow, and/or Airflow; manage experiments & model registry.
• Deploy and manage LLM/ML inference with vLLM, Triton, or TGI on local GPU hardware.
• Administer Kubernetes on bare-metal on-premise infrastructure; manage NVIDIA GPU Operator, Helm, RBAC, and storage.
• Configure and maintain NVIDIA GPU infra: CUDA, cuDNN, TensorRT, NCCL, drivers, and multi-GPU scheduling.
• Build CI/CD pipelines for model code; automate testing, validation, and deployment via IaC.
• Set up model monitoring dashboards: accuracy, data drift, and performance alerts (Prometheus, Grafana).
• Manage air-gapped environments: offline repos, private registries, RBAC, audit logging, security controls.
• Implement HA, DR, backup, and incident management for the ML platform.

Skills

MLOps

Kubernetes (bare metal)

GPU clusters

CI/CD

Python scripting

Education

B.Tech / M.Tech in Computer Science, Software Engineering, or related field

Tools

Docker

Kubernetes

Terraform

Ansible

Helm

MLflow

Kubeflow

Airflow

Prometheus

Grafana

Job description

Job Description

Senior MLOps & DevOps Engineer

Location Bhopal, MP (On-site)

Experience 610 Years

Reports To Lead AI Engineer

Openings 1

Role Overview

Own and operate the end-to-end MLOps platform for enterprise AI/ML solutions on secure on-premise and air-gapped infrastructure. This is not a traditional DevOps role hands-on production experience with GPU clusters (H100/A100), Kubernetes, LLM inference platforms (vLLM/Triton), and air-gapped deployments is mandatory. The ideal candidate can stand up the full MLOps stack from bare metal without internet access and has operated AI/ML systems in defence, government, or classified environments.

Key Responsibilities

• Design and operate the full ML pipeline: ingestion training validation deployment monitoring drift detection automated retraining.
• Build and maintain ML pipelines with MLflow, Kubeflow, and/or Airflow; manage experiment tracking, model registry, and versioning.
• Deploy and manage LLM/ML inference with vLLM, Triton, or TGI on local GPU hardware.
• Administer Kubernetes on bare-metal on-premise infrastructure; manage NVIDIA GPU Operator, Helm, RBAC, and storage integration.
• Configure and maintain NVIDIA GPU infrastructure: CUDA, cuDNN, TensorRT, NCCL, GPU drivers, and multi-GPU scheduling.
• Build CI/CD pipelines for model code; automate testing, validation, and deployment via IaC (Terraform, Ansible, Helm).
• Set up model monitoring dashboards: accuracy tracking, data drift detection, and performance alerting (Prometheus, Grafana).
• Manage air-gapped environments: offline package repos, private container registries, RBAC, audit logging, and security controls.
• Implement HA, disaster recovery, backup, and incident management for the ML platform.

Required Skills & Experience

• 6-10 years in DevOps/MLOps; 3+ yrs DevOps, 2+ yrs dedicated MLOps with production GPU infrastructure.
• Hands-on NVIDIA GPU ops: H100, H200, A100, L40S or RTX Ada; CUDA, cuDNN, TensorRT, NCCL, multi-GPU clusters.
• Production experience: MLflow, Kubeflow/Airflow, Docker, Kubernetes (bare metal), CI/CD, and IaC (Terraform/Ansible/Helm).
• LLM inference platforms: vLLM, NVIDIA Triton Inference Server, Ollama, or TGI on local GPU hardware.
• Proven experience operating AI/ML in air-gapped, classified, defence, or government environments with offline registries and repos.
• Ability to provision the complete local ML stack from bare metal (GPU K8s registry inference monitoring) without internet access.
• Python automation, Linux administration, FastAPI/Flask model serving, Prometheus/Grafana monitoring.
• Security controls: RBAC, secrets management, network policies, audit logging on on-premise infrastructure.

Preferred / Good to Have

• DGX-class systems (DGX A100/H100); MinIO/Ceph distributed object storage; DVC for data versioning.
• HA/DR configuration for ML serving; RPA platform integration; model governance frameworks.

Qualifications

• B.Tech / M.Tech in Computer Science, Software Engineering, or related field.
• Kubernetes (CKA) or MLOps certifications are a plus.