**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
