ML / LLMOps Engineer

SentraAI; Dubai; On-site; AED 250,000 - 350,000; Full time

Job summary

SentraAI in Dubai is seeking an LLM / MLOps Engineer to operationalize machine learning and LLM systems. The role is focused on ensuring AI systems are reliable, observable, and controllable in production.

Key responsibilities include building ML pipelines, enforcing monitoring for performance, and ensuring governance standards are met. You will collaborate closely with AI/ML engineers and data teams to ensure quality delivery.

Qualifications

• Build and operate end-to-end ML and LLM pipelines from training through inference.
• Manage model registries and promote workflows.
• Automate training and experiment tracking using Amazon SageMaker.

Responsibilities

• Design and operate inference services for real-time and batch workloads.
• Implement rollout strategies and ensure system performance.
• Embed validation and controls for LLM-based systems.

Skills

Strong proficiency in Python

CI/CD for ML systems

Distributed systems fundamentals

MLflow or Weights and Biases

Docker

FastAPI or equivalent frameworks

Kubernetes or managed equivalents

LLM-based applications

AWS, Azure, or GCP

Job description

Dubai, United Arab Emirates | Posted on 06/29/2026

As an LLM / MLOps Engineer at SentraAI, you are responsible foroperationalising, deploying, and governing machine learning and LLM-based systems in production.

This role exists to ensure AI systems are reliable, observable, reproducible, and controllable once live. You will own pipelines, deployment patterns, monitoring, and controls that allow AI systems to operate safely within enterprise run‑states.

This is a systems and platform‑focused role, not a research position.

About SentraAI

SentraAI is a specialist enterprise AI firm, focused on helping large, regulated organisations move AI and data platforms from experimentation into production safely and sustainably.

We work inside enterprise run‑states, where governance, operational risk, change control, and long‑term ownership are integral to delivery. Our teams are trusted to design and deliver systems, platforms, and operating models that can be run, audited, and evolved, not just launched.

We prioritise engineering discipline, architectural clarity, and delivery quality over speed theatre or hype.

Requirements

LLM and ML Operations

• Build and operate end‑to‑end ML and LLM pipelines from training through inference
• Manage model registries, versioning, and promotion workflows
• Package and deploy models using containerised and cloud‑native patterns
• Automate model training, hyperparameter tuning, and experiment tracking using Amazon SageMaker, SageMaker Pipelines, and SageMaker Experiments
• Deploy ML models to production using SageMaker Hosting, SageMaker Serverless Inference, or containerised services (Amazon ECS/EKS, Fargate, or AWS Lambda)

Deployment, Reliability, and Scale

• Design and operate inference services for real‑time and batch workloads
• Implement rollout, rollback, and blue/green deployment strategies
• Ensure systems meet performance, availability, and cost expectations

Observability and Governance

• Implement monitoring for model performance, drift, and anomalous behaviour
• Instrument AI systems for logging, metrics, and traceability
• Embed validation, guardrails, and runtime controls for LLM‑based systems
• Support audit, risk, and compliance requirements

Platform and Collaboration

• Work closely with AI/ML Engineers to productionise models
• Partner with data engineering, security, and platform teams
• Contribute to platform standards, runbooks, and reference architectures

Required Qualifications

Core Engineering Capability

• Strong proficiency in Python for production systems
• Experience with CI/CD and automation for ML systems
• Strong understanding of distributed systems fundamentals

MLOps and LLM Ops Tooling

• MLflow or Weights and Biases
• Docker and containerised deployments
• FastAPI or equivalent inference frameworks
• Kubernetes or managed equivalents

LLM Operations

• Experience deploying and operating LLM‑based applications
• Familiarity with vector databases such as Pinecone, Weaviate, or FAISS
• Understanding of prompt orchestration and runtime controls
• Strong experience with AWS, Azure, or GCP

Advantageous but Not Mandatory

• Experience operating AI systems in regulated enterprise environments
• Exposure to AI security, guardrails, or red‑team practices
• Experience with cost optimisation or FinOps for AI workloads
• Familiarity with SOC, monitoring, or incident response processes