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

1. 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

-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.

1. 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

1. 3. CI/CD & Automation

- Design and implement CI/CD pipelines for ML models, APIs, and data workflows
- Enable automated testing, deployment, and release management

1. 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

1. 5. Data & System Integration

- Build pipelines integrating structured databases and high-volume logs/streaming data
- Support batch and real-time inference architectures

1. 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

1. 7. GenAI & Advanced ML Systems

- Deploy RAG pipelines and vector databases
- Manage LLM serving frameworks
- Work with agent orchestration frameworks

1. 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

-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

Valuable 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.

About Bounteous

Bounteous is a global AI Services firm where agentic engineering and human experience converge to deliver transformative business outcomes for the enterprise. We help organizations design, build, and scale AI-driven products, platforms, and processes.

With more than 5,000 team members worldwide, Bounteous helps organizations take AI from experimentation to enterprise scale.

Learn more at www.bounteous.com

📌 Senior MLOps + DevOps Engineer (Hyderabad)
🏢 Accolite Digital India
📍 Hyderabad