JOB ID:QT-SNB-08-358

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

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

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.

Disclaimer: This job posting and Location has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Sr MLOps + DevOps Engineer (On-Prem AI Platform) (Nagpur)
🏢 Qloron Technology
📍 Nagpur