GenAI Engineer

Deeplight; United Arab Emirates; On-site; AED 90,000 - 130,000; Full time

Job summary

A leading AI consultancy in the United Arab Emirates is seeking a Generative AI Engineer to design and implement high-impact AI solutions. The role involves optimizing models and enhancing LLM systems within a robust MLOps framework. We are looking for candidates with deep expertise in Python and advanced machine learning techniques to drive transformative AI projects forward.

Benefits

Competitive salary

Comprehensive health insurance

Ongoing professional development

Flexible working arrangements

Career advancement opportunities

Qualifications

• 3–7 years in AI/ML with hands‑on experience in LLMs.
• Experience delivering production‑grade GenAI tools.
• Familiarity with data ingestion tools like Kafka and Airflow.

Responsibilities

• Design and deliver GenAI solutions based on LLMs.
• Optimize model performance using fine-tuning techniques.
• Implement MLOps automation using major cloud platforms.

Skills

Deep knowledge of transformers

Proficiency in Python

Experience with LLMs

Mastery of Docker and Kubernetes

Education

BSc/MSc in Computer Science or Data Science

Tools

PyTorch

TensorFlow

Docker

Kubernetes

Terraform

Job description

DeepLight AI is a specialist consultancy implementing intelligent enterprise systems, with deep expertise in financial services and banking.

We are seeking a Generative AI Engineer to join our team, focusing on turning advanced research into high-impact, production-ready enterprise solutions.

You won't just train models; you will design, build, and own the entire lifecycle of AI systems that leverage Large Language Models (LLMs), RAG, and Multimodal AI to solve our clients' most complex problems.

The Challenge: Owning the LLM Production Pipeline. This role demands a unique combination of applied machine learning, data engineering, and scalable software delivery, operating entirely within a robust MLOps/LLMOps framework.

Key Responsibilities

• Design & Deliver GenAI Solutions: Leading the implementation of LLM-based applications, including custom chatbots, advanced summarization services, and creative co-pilots.
• Model Optimization & Steering: Applying cutting‑edge techniques like fine‑tuning, LoRA/PEFT, and RLHF to optimize model performance and ensure factual accuracy.
• RAG System Architecture: Architecting and building high-performance Retrieval‑Augmented Generation (RAG) pipelines, managing the full lifecycle of embeddings and context-aware retrieval.
• Production Code: Writing robust, deployment‑ready software in Python and TypeScript, delivering clean, modular code.
• MLOps Automation: Implementing full LLMOps/MLOps using Docker, Kubernetes, and Terraform to automate CI/CD, deployment, and versioning across major cloud platforms (AWS/GCP/Azure).
• Data Grounding: Ensuring model intelligence is fresh and reliable by connecting to Airflow, dbt, and Kafka pipelines.
• Governance & Safety: Embedding responsible AI practices, including hallucination control, bias mitigation, and auditability, into every deployed system.

Benefits & Growth Opportunities

• Competitive salary
• Comprehensive health insurance
• Ongoing professional development
• Opportunity to work on cutting‑edge AI projects
• Flexible working arrangements
• Career advancement opportunities in a rapidly growing AI company

This position offers a unique opportunity to shape the future of AI implementation while working with a talented team of professionals at the forefront of technological innovation.

The successful candidate will play a crucial role in driving our company's success in delivering transformative AI solutions to our clients.

Requirements

• Deep knowledge of transformers, diffusion models, and advanced prompt engineering.
• Proven expertise in embeddings, vector databases, and designing context‑aware retrieval pipelines.
• Proficiency in Python, REST APIs, containerization, CI/CD, and model versioning.
• Proficiency in Python and TypeScript; core ML development using PyTorch, TensorFlow, and the Hugging Face ecosystem.
• Expertise with LangChain, LlamaIndex, and APIs from OpenAI/Anthropic.
• Use of MLflow and Weights & Biases for experiment tracking and versioning, leveraging platforms like Vertex AI or SageMaker.
• Mastery of Docker, Kubernetes, and Terraform for scalable deployment across major clouds (AWS, GCP, Azure).
• Familiarity with Airflow, dbt, Postgres, and Kafka for data ingestion and transformation.
• Experience & Qualifications: 3–7 years in AI/ML, with a critical 1–3 years hands‑on with LLMs or production GenAI applications.
• Proven delivery of production‑grade GenAI tools in agile, rapid‑iteration environments.
• BSc/MSc in Computer Science, Data Science, or a related technical field.
• Experience with model evaluation, data privacy, and bias mitigation.
• Hands‑on experience with ChromaDB, Pinecone, Weaviate, and FAISS for retrieval and embedding management.

Please note: If you fail to meet the required criteria in the screening questions, your application will not be progressed.