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Senior / Principal AI & Machine Learning Engineer

SaltDubai, UAEYesterdayMid-Seniorfulltime
GoScalaMachine LearningDeep Learning
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About This Role

**HIRING**

Role: Senior / Principal AI & Machine Learning Engineer (Product-Focused)

Location: UAE (Hybrid / On-site depending on client)

Salary: AED 40,000 – 70,000+ Per Month (depending on seniority, impact, and ownership)

Type: Permanent / Senior Contract Options

Overview

I am partnering with a high-growth, product-led organisation in the UAE building next-generation AI systems across large-scale data platforms, applied machine learning, and LLM-driven products.

This is not an “analytics support” or research-only role. This is a deeply hands-on engineering position for someone who builds, and scales production AI systems used by real users in real-time environments.

I am looking for Senior to Principal-level AI/ML Engineers who operate as technical owners of AI products — from model design through to deployment, optimisation, and ongoing performance in production.

What You’ll Be Doing

You will act as a technical leader and builder within a cross-functional AI product team, responsible for taking machine learning systems from concept through to production at scale.

Core responsibilities include:

  • Designing and building end-to-end machine learning systems in production environments
  • Developing and deploying scalable AI/ML models (supervised, unsupervised, deep learning, and generative AI use cases)
  • Leading architecture decisions for ML pipelines, feature stores, training infrastructure, and inference systems
  • Building and optimising LLM-based applications (RAG systems, fine-tuning, prompt engineering, evaluation pipelines)
  • Owning MLOps practices: CI/CD for ML, model versioning, monitoring, drift detection, and retraining pipelines
  • Collaborating closely with product, engineering, and data teams to translate business problems into scalable AI solutions
  • Improving model performance, latency, cost efficiency, and reliability in production
  • Mentoring engineers and setting technical standards across AI/ML teams
  • Contributing to platform-level decisions around data, AI infrastructure, and cloud architecture

Candidate Profile

  • 7–15+ years' experience in software engineering, data science, or ML engineering
  • Strong background in product companies, scale-ups, or enterprise AI platforms
  • Previously built production-grade AI systems, not just notebooks or POCs
  • Comfortable owning systems end-to-end (data → model → deployment → monitoring)
  • Often ex-FAANG, major tech firms, AI startups, or high-performing regional tech companies
  • Strong engineering mindset (not just modelling)
  • Product-first engineering, not research-only profiles
  • Strong Python engineering with systems thinking
  • Used to fast iteration and deploying models into live environments
  • Comfortable working directly with stakeholders and product owners

Core Technical Stack

Machine Learning / AI

  • PyTorch
  • TensorFlow
  • XGBoost / LightGBM
  • Hugging Face ecosystem (Transformers, Datasets, Diffusers)
  • OpenAI / Anthropic APIs (production LLM integration)
  • LangChain / LlamaIndex (agentic workflows, RAG systems)

LLM / GenAI Stack (very important in 2026)

  • RAG architectures (vector DB + retrieval pipelines)
  • Embedding models (OpenAI, Cohere, open-source models)
  • Vector databases: Pinecone, Weaviate, Milvus, FAISS
  • Fine-tuning frameworks (LoRA, PEFT)
  • Evaluation frameworks (RAGAS, custom eval pipelines)

MLOps / Production

  • Docker, Kubernetes
  • MLflow / Weights & Biases
  • Airflow / Dagster / Prefect
  • CI/CD tools (GitHub Actions, GitLab CI)
  • Model monitoring (Evidently AI, Arize, custom observability stacks)

Cloud Platforms

  • AWS (SageMaker, EKS, S3, Lambda) or Azure ML / Azure Databricks

Data Stack

  • Databricks
  • Spark (PySpark essential)
  • Delta Lake / Iceberg / Lakehouse architectures

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