Senior MLOps & DevOps Platform Architect On-Prem

Telcovas Solutions & Services; Dubai; On-site; AED 350,000 - 480,000; Full time

Job summary

Telcovas Solutions & Services in Dubai is seeking a Senior MLOps + Dev Ops Engineer with 8+ years’ experience to architect, build, and scale AI/ML platforms in an on-prem enterprise environment. You will own ML systems end-to-end, covering infrastructure, CI/CD, and production reliability.

The role emphasizes platform engineering, GPU-based deployment, and secure, scalable ML workflows in restricted environments. Strong Python, Docker, Kubernetes, and CI/CD expertise are required.

Qualifications

• 8+ years in MLOps / Dev Ops / Platform Engineering.
• Proven experience scaling production ML systems.
• Experience deploying ML/LLM systems in production.

Responsibilities

• Platform Architecture & Ownership - Design and own end-to-end ML platform architecture across data, training, deployment and monitoring.
• Model Deployment & Serving - Deploy and manage ML/LLM models on GPU-based on-prem infrastructure; optimize inference performance.
• CI/CD & Automation - Design and implement CI/CD pipelines for ML models, APIs, and data workflows.
• Infrastructure & Containerization - Manage Linux-based on-prem infrastructure; containerize apps with Docker; deploy with Kubernetes/OpenShift.
• Data & System Integration - Build pipelines for structured data and high-volume logs; support batch and real-time inference.
• Monitoring, Observability & Reliability - Implement end-to-end observability; use Prometheus, Grafana, ELK; ensure SLA adherence.
• Gen AI & Advanced ML Systems - Deploy RAG pipelines and vector databases; manage LLM serving frameworks.
• Leadership & Collaboration - Mentor engineers; collaborate with cross-functional teams; drive production readiness.

Skills

Python

Bash scripting

ML lifecycle

Production ML

Tools

Docker

Kubernetes/OpenShift

Jenkins

GitLab CI

Job description

Telcovas Solutions & Services in Dubai is seeking a Senior MLOps + Dev Ops Engineer with 8+ years’ experience to architect, build, and scale AI/ML platforms in an on-prem enterprise environment. You will own ML systems end-to-end, covering infrastructure, CI/CD, and production reliability.

The role emphasizes platform engineering, GPU-based deployment, and secure, scalable ML workflows in restricted environments. Strong Python, Docker, Kubernetes, and CI/CD expertise are required.