Machine Learning Engineering Manager

Engineering
United Arab Emirates
March 8, 2026
Full Time

Job Overview

Job Description

411_3287870

  • Build and maintain scalable AI/ML pipelines for pretraining, fine-tuning, inference, and continuous learning, integrating advanced data preprocessing and retrieval-augmented generation (RAG) techniques.
  • Develop APIs and embeddings-based integrations to seamlessly connect LLMs and GenAI models with enterprise applications, knowledge bases, and workflows.
  • Implement prompt engineering and model orchestration strategies to enhance performance, control AI behaviour, and mitigate hallucinations in production environments.
  • Monitor, evaluate, and troubleshoot AI models for performance, fairness, and compliance, leveraging automated feedback loops, human-in-the-loop (HITL) frameworks, and explainability techniques.
  • Conduct research on the latest advancements in GenAI and LLM architectures (Mixture of Experts, Transformer optimizations, multimodal models, etc.) to drive innovation and refine AI strategy.
  • Collaborate with data engineering and MLOps teams to ensure robust data pipelines, vector databases, and scalable cloud infrastructure for AI deployment.
  • Leverage Azure AI services and cloud platforms (Azure OpenAI, Azure Machine Learning, Databricks, and Vector Search) for model training, deployment, and real-time inference.
  • Implement best practices for AI governance, reproducibility, and CI/CD automation to streamline the model lifecycle while ensuring security, compliance, and ethical AI.

Key Expertise Areas

  • Strong understanding of transformer architectures (GPT, BERT, T5) and LLM fine-tuning.
  • Experience with prompt engineering, RAG (Retrieval-Augmented Generation), and embeddings.
  • Proficiency in NLP, deep learning, and generative AI techniques.
  • Knows how to build agentic architectures & AI-agents.
  • Hands‑on experience with Azure OpenAI Service, Azure AI Search, and Azure Cognitive Services.
  • Familiarity with Azure Machine Learning (Azure ML) for model training, fine‑tuning, and deployment.

Software Engineering & MLOps

  • Proficiency in Python, PyTorch, TensorFlow, Hugging Face, LangChain, and LlamaIndex.
  • Experience deploying AI models using Azure Functions, API Management, and Kubernetes (AKS).
  • Strong grasp of Azure SDK, DevOps, and CI/CD for AI pipelines.
  • Knowledge of Azure Blob Storage, Cosmos DB, and vector databases for LLM‑powered applications.
  • Experience with scalable AI architectures and GPU optimization.

Security, Compliance & Responsible AI

  • Understanding of data privacy, model security (prompt injection, hallucination mitigation), and Microsoft Purview.
  • Experience in deploying enterprise‑grade AI applications with ethical AI best practices.

Qualification and Experience

  • Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
  • Over 10 years of experience in the field, with a minimum of 5 years of experience as an AI Developer, with a strong focus on building and deploying machine learning models.
  • Experience in the implementation of solutions using generative AI models (OpenAI services including GPT and embedding models and other popular open‑source models).
  • Proficiency in programming languages such as Python, R, or Java, and experience with ML frameworks like TensorFlow, Keras, or PyTorch.
  • Demonstrated experience in implementing ML models in real‑world scenarios, with case studies or portfolio examples.
  • Experience in improving model performance including optimization techniques, hyperparameter tuning, performance monitoring.
  • Strong understanding of data structures, data modelling, and software architecture.
  • Experience with cloud platforms, particularly Microsoft Azure, is highly desirable.
  • Experience with low‑code/no‑code platforms is highly desirable.
  • Good understanding of data management principles, data warehousing, and business intelligence concepts.
  • Excellent analytical, problem‑solving, and communication skills.
  • Ability to work collaboratively in a team environment.
  • Ability to work in a fast‑paced and dynamic environment.
  • Ability to learn new technologies and tools quickly.

#J-18808-Ljbffr

2026-02-27 08:36:14

Related Jobs