Senior MLOps + DevOps Engineer

Bounteous; Hyderabad; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

Bounteous seeks a Senior MLOps + DevOps Engineer to architect, build, and scale AI/ML platforms in an on-prem enterprise environment. The role requires end-to-end ownership of ML systems, infrastructure, CI/CD, and production reliability, enabling scalable deployment of ML and GenAI solutions.

The candidate will work with Linux-based on-prem infrastructure, Docker containers, and orchestration with Kubernetes/OpenShift, focusing on secure, scalable, and observable ML deployments.

Qualifications

• Strong Python and scripting (Bash) abilities.
• Deep understanding of ML lifecycle and production.
• Experience deploying ML/LLM systems in production.
• SQL and data pipeline experience.
• Explain real-world integration with Kafka (on‑premise).
• Ability to design scalable ML/data platforms end‑to‑end.

Responsibilities

• Define and enforce scalable ML systems best practices.
• Standardize MLOps and DevOps frameworks and processes.
• Architect AI/ML platform and explain it clearly.
• Design scalable ML/data platforms end-to-end.
• Explain on-prem integration patterns using Kafka.
• Deploy ML models on on-prem GPU infrastructure.
• Implement CI/CD pipelines for ML models and data workflows.
• Operate Linux on-prem and containerize using Docker.
• Deploy orchestrations with Kubernetes/OpenShift.
• Build data pipelines and support batch/real-time inference.
• Ensure observability, reliability, and incident response.
• Mentor engineers on MLOps best practices.

Skills

Python

Bash scripting

ML lifecycle

Production ML

SQL

Kafka integration

OpenShift

Tools

Docker

Kubernetes

OpenShift

Terraform

Ansible

Job description

5 to 9 years of experience
Skills: MLOps + DevOps, AI/ML platforms, Linux, Docker, Kubernetes

Role Overview

We are looking for a Senior MLOps + DevOps Engineer (6+ 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

• Define and enforce best practices for scalable and secure ML systems
• Standardize MLOps + DevOps frameworks and processes
• AI/ML Platform Architecture deployed and should be able to explain it clearly.
• Ability to design scalable ML/data platforms end-to-end
• Explain real-world integration patterns using Kafka, especially on-premise setups.

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
• Understanding of OpenShift AI ecosystem

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)
• 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
• 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
• SQL and data pipeline experience
• Explain real-world integration patterns using Kafka, especially on-premise setups.
• Should be able to clearly explain Gunicorn
• Data ingestion pipelines (real-time and batch)
• AI/ML Platform Architecture deployed and should be able to explain it clearly.
• Understanding of OpenShift AI ecosystem
• Ability to design scalable ML/data platforms end-to-end

Good to Have

• GPU optimization knowledge
• MLflow / Kubeflow
• Terraform / Ansible
• Experience in on-prem or restricted environments

Experience

• 6+ 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.

I have flexibility to create happiness and harmony. Our working environment, plus the ability to work remotely, has eliminated the Sunday Scaries that plagued me for decades in my career. I actually say “Happy Monday” on my calls.

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