A dedicated startup being formed to industrialize and scale a secure, AI-enabled, multi-source decision-support platform is seeking an experienced MLOps Engineer in Abu Dhabi. The platform is a multi-sensor fusion and agentic AI solution connecting to geospatial layers, imagery, video, and operational signals. This role designs and operates end-to-end ML/LLM delivery pipelines, builds CI/CD for models and services, standardizes experiment tracking, implements monitoring and observability, and optimizes inference performance and cost across a high-growth, startup-mode environment.
About This AI Startup Opportunity
Mission: Industrialize and scale a secure, AI-enabled, multi-source decision-support software offering
Platform: Multi-sensor fusion and agentic AI solution — geospatial, imagery, video, and operational signals
Stage: Dedicated startup being formed — ground-floor opportunity to shape processes, standards, and culture
Environment: Startup mode — hands-on, flexible, comfortable pivoting, and able to unblock teams quickly
Key Performance Areas for This MLOps Role
Deployment Frequency: Lead time for model releases and deployment pipeline velocity
Production Stability: Incident rate, MTTR, and SLO compliance across all production systems
Model Quality Health: Drift detection coverage and evaluation gate pass rates
Inference Efficiency: Cost and latency improvements through batching, caching, and quantization
Position Overview
This MLOps Engineer role designs and operates end-to-end ML/LLM delivery pipelines from data through training, fine-tuning, evaluation, packaging, and deployment. You will build CI/CD for models and services with automated testing and rollback strategies, standardize experiment tracking and model lineage, implement monitoring for latency, drift, and quality signals, optimize inference performance and cost, define environment management across dev/stage/prod, work with engineering on scalable serving patterns, and support release readiness including runbooks, SLOs, and post-release stability tracking.
Why This Role Matters: As MLOps Engineer at this Abu Dhabi AI startup, you build the ML infrastructure backbone for a multi-sensor, agentic AI platform from scratch, design LLM delivery pipelines end-to-end, own production observability and inference optimization, work hands-on in a fast-moving startup environment where your decisions directly shape the platform’s scalability and reliability, and join a founding technical team building something genuinely new in the UAE AI ecosystem.
Key Responsibilities
ML/LLM Pipeline Design & CI/CD
- Design and operate end-to-end ML/LLM delivery pipelines: data → training/fine-tuning → evaluation → packaging → deployment
- Build CI/CD for models and services including automated testing, validation gates, and rollback strategies
- Standardize experiment tracking, model/version lineage, and artifact management (datasets, prompts, checkpoints, embeddings)
Monitoring, Observability & Inference Optimization
- Implement monitoring and observability: latency, cost, drift, quality signals, and safety/guardrails metrics
- Optimize inference performance and cost through batching, caching, quantization, and hardware choices
- Define and enforce environment and dependency management across dev, stage, and production
Engineering Integration & Release Readiness
- Work with engineering on scalable serving patterns (APIs, streaming, event-driven) and with security on access controls
- Support release readiness: runbooks, incident response, SLOs/SLAs, and post-release stability tracking
- Coordinate with procurement and legal for tooling, cloud services, and vendor onboarding
Qualifications & Requirements
Experience Requirements
- Typically 5+ years in MLOps, DevOps, or Data Platform roles
- Proven production deployments of ML and/or LLM-powered systems
- Experience in fast-paced product or startup environments preferred
Tools & Technical Skills
- ML lifecycle: MLflow, Weights & Biases, or equivalent
- Serving: FastAPI; Triton and Ray Serve are a plus
- Orchestration: Airflow or Dagster (plus)
- Observability: Prometheus/Grafana, OpenTelemetry, ELK stack
- Cloud: AWS, Azure, GCP, or private cloud
- Containers and orchestration: Docker; Kubernetes is a plus
- CI/CD and automation: GitHub Actions, GitLab CI, or Jenkins; Terraform is a plus
- Experience with model serving patterns (REST/gRPC) and cost management practices
About This Opportunity — Founding-Stage AI Platform in Abu Dhabi
Join a dedicated startup being formed in Abu Dhabi to industrialize and scale a secure, AI-enabled, multi-source decision-support platform. Built on multi-sensor fusion and agentic AI, the platform connects geospatial layers, imagery, video, and operational signals in a single intelligent system. This MLOps Engineer role is a ground-floor opportunity to shape the ML infrastructure, pipeline standards, and operational practices for a high-growth AI startup in the heart of the UAE’s rapidly expanding technology ecosystem.
Career Excellence: Build the MLOps foundation for a next-generation agentic AI platform from the ground up in Abu Dhabi.
Who Should Apply?
- Senior MLOps Engineers: With 5+ years of production ML and LLM system deployment experience
- LLM Pipeline Specialists: Experienced managing the full model lifecycle from training to serving
- Cloud ML Infrastructure Engineers: Proficient with AWS, Azure, or GCP in ML workload contexts
- Observability & Monitoring Practitioners: With Prometheus, Grafana, and OpenTelemetry experience
- Startup-Minded Engineers: Comfortable with ambiguity, pivoting quickly, and hands-on ownership in a founding team
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