AI Engineer

Tanqeeb; Abu Dhabi; On-site; AED 350,000 - 600,000; Full time

Job summary

Tanqeeb is seeking an experienced AI/ML engineer to design, develop and deploy scalable generative AI solutions for internal teams. You will manage end-to-end LLM deployments, train models, and optimize GPU resources for high-efficiency inference.

The role calls for strong Python, MLOps, and data engineering skills, with emphasis on RAG pipelines, vector databases, and Airflow orchestration in production environments.

Qualifications

• BS degree in CS/SE/AI or related field is required.
• 8+ years in software engineering with production LLM/RAG experience.
• Strong Python, DBs and ML framework experience expected.

Responsibilities

• Design robust RAG pipelines integrating LLMs with proprietary data sources.
• Architect and maintain Apache Airflow DAGs for data workflows.
• Manage and optimize Vector DBs for embeddings storage and retrieval.
• Maintain internal LLM deployments with high uptime and GPU resource management.
• Train, fine-tune and deploy DL/LLM models to production environments.

Skills

Python

Java

PyTorch

TensorFlow

Hugging Face

RAG

LangChain

LlamaIndex

LoRA/PEFT

Apache Airflow

DAGs

PySpark

Ray

Vector DBs

SQL/NoSQL

Embeddings

Semantic search

Docker

Kubernetes

Cloud (AWS/Azure/GCP)

CI/CD pipelines

CUDA

Education

Bachelor's Degree in CS/Software/AI or related field

Tools

PyTorch

TensorFlow

Hugging Face

LangChain

Llama Index

Apache Airflow

Docker

Kubernetes

GPU optimization tools

Job description

About the Role

You will be designing, developing and deploying scalable generative AI solutions while managing and maintaining LLM deployments for internal AI tools, ensuring high availability and efficiency for internal teams, and training and deploying deep learning and LLMs models to production.

Responsibilities

Design and implement robust RAG pipelines to integrate Large Language Models (LLMs) with proprietary data sources, ensuring high accuracy and low latency in responses. Architect and maintain complex Apache Airflow DAGs to automate data ingestion, cleaning, embedding generation, and model retraining workflows. Manage and optimize Vector Databases (e.g., Pinecone, Milvus, Weaviate) for efficient storage and retrieval of high-dimensional embeddings. Maintain and monitor internal LLM deployments (hosting, scaling, and versioning), ensuring 99% uptime, managing GPU resources and optimizing inference for internal AI usage. Train, fine-tune, and deploy deep learning and LLM models to production environments, managing the full MLOps lifecycle from experimentation to serving.

Qualifications

At least Bachelor's Degree in Computer Science, Software Engineering, Data Science, AI or related field8+ years in Software Engineering overall Solid experience in building LLM applications and RAG pipelines, in production Production experience with Python and DBs. Optimizing GPU compute resources (v LLM, Tensor RT). Open-source contributions to AI/MLOps libraries. Advanced model fine-tuning (LoRA, PEFT). Good foundation in linear algebra and statistics.

Required Skills

• Programming: Python (Must)
• Java (nice to have: GO, Scala)
• AI/ML: PyTorch, Tensor Flow, Hugging Face
• Gen AI and LLMs: RAG, Lang Chain, Llama Index, RAG pipeline design
• Nice to have: PEFT/LoRA fine-tuning
• Data Processing: Apache Airflow, DAGs
• Nice to have: PySpark or Ray
• Databases: Vector DBs (Pinecone/Milvus), SQL/NoSQL, embedding models and semantic search
• MLOps: Docker, Kubernetes, v LLM, Cloud (AWS/Azure/GCP), CI/CD pipelines
• Nice to have: GPU resource management and optimization (CUDA)