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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: - Solid 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: - 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.

Pay: ₹50,000.00 - ₹110,000.00 per month

Work Location: In person

📌 Senior MLOps + DevOps Engineer (Pune)
🏢 RCV World
📍 Pune