Senior AI Engineer

Rheinmetall Barzan Advanced Technologies QSTP-LLC; Doha; On-site; QAR 437,000 - 655,000; Full time

Benefits offered by this job

Competitive compensation

Cutting-edge projects

Career development

Job summary

Rheinmetall Barzan Advanced Technologies QSTP-LLC in Doha is seeking a Senior AI Engineer to lead the full lifecycle of AI projects, from research to production-grade systems, including edge and embedded platforms.

You will design, train, and deploy models across ML, DL, NLP, CV, and generative AI, and mentor junior engineers while collaborating with product, data, and software teams. The role offers exposure to cutting-edge AI initiatives in a fast-paced environment.

Qualifications

• Minimum Bachelor's degree in CS/AI or related field.
• Master's/PhD in AI/ML or data science preferred.
• Proven, documented experience designing, developing, and deploying advanced AI solutions in real-world use cases.
• Demonstrated track record of taking AI projects from PoC to production.
• Strong problem-solving, communication, and technical leadership skills.

Responsibilities

• Design, train, and optimize AI models across ML, DL, RL, NLP, CV, and generative AI.
• Build and fine-tune Transformer-based models and LLMs.
• Develop agentic AI systems and multi-step reasoning pipelines.
• Apply transfer learning and PEFT to domain adaptation.
• Scale PoCs into production-grade solutions.
• Optimize for inference with quantization, pruning, distillation.
• Deploy on GPU workstations and edge/embedded platforms.
• Define benchmarking and testing for accuracy, latency, and resources.
• Deploy models and integrate with APIs and enterprise infra.
• Collaborate with cross-functional teams and mentor junior engineers.
• Stay current with AI research and recommend tools.

Skills

Machine Learning

Deep Learning

NLP

Computer Vision

Generative AI

Transformer architectures

PyTorch

TensorFlow

ONNX

MLOps

Docker

Kubernetes

Education

Bachelor's degree in CS/AI related field

Master's or PhD in AI/ML/Data Science

Tools

CUDA

TensorRT

OpenCV

Hugging Face Transformers

ONNX Runtime

REST APIs

gRPC

Job description

We are seeking a Senior AI Engineer with a proven record of designing, developing, and deploying advanced AI solutions to real-world problems. You will lead the full lifecycle of AI projects, from research and prototyping to production-grade systems, including solutions running on workstations and edge or physical AI platforms.

Key Responsibilities

• Design, train, and optimize AI models across machine learning, deep learning, reinforcement learning, NLP, computer vision, and generative AI.
• Build and fine-tune Transformer-based architectures, diffusion models, and large language models (LLMs).
• Design and implement agentic AI systems and AI workflows, including orchestration, tool use, and multi-step reasoning pipelines.
• Apply transfer learning, fine-tuning, and parameter-efficient techniques to adapt models to specific domains.
• Scale AI proofs of concept (PoCs) into robust, maintainable, product-level solutions.
• Optimize models for efficient inference through quantization, pruning, distillation, and hardware-aware tuning.
• Develop and deploy AI solutions on GPU workstations and edge or embedded platforms, including physical AI applications.
• Define and run benchmarking and testing frameworks to evaluate model accuracy, robustness, latency, throughput, and resource usage.
• Deploy models and integrate them with existing systems, APIs, and enterprise infrastructure.
• Collaborate with cross-functional teams (product, data, software, and domain experts) and mentor junior engineers.
• Stay current with AI research and recommend new techniques and tools.

Required Technical Skills

Core AI Domains

• Machine Learning: supervised and unsupervised learning, feature engineering, model selection, and evaluation.
• Reinforcement Learning: policy optimization, reward design, and applied RL for decision-making or control.
• Deep Learning and Neural Networks: CNNs, RNNs, Transformers, and attention-based architectures.
• Natural Language Processing: language modeling, text classification, retrieval, embeddings, and conversational systems.
• Computer Vision: detection, segmentation, tracking, and multimodal vision-language models.
• Generative AI: LLMs, diffusion models, prompt engineering, fine-tuning, and retrieval-augmented generation (RAG).
• Agentic AI and AI Workflows: designing autonomous, tool-using, multi-agent pipelines.

Optimization and Performance

• Model quantization, pruning, and knowledge distillation.
• Transfer learning and efficient fine-tuning (e.g., LoRA, PEFT).
• Benchmarking and performance testing: accuracy metrics, latency, throughput, memory footprint, stress and regression testing, and reproducible evaluation pipelines.

Frameworks and Libraries

• PyTorch, TensorFlow, Scikit-learn, Hugging Face Transformers, ONNX, OpenCV, and related tools.

Workstation, Edge, and Physical AI

• GPU computing (CUDA), inference runtimes (TensorRT, ONNX Runtime, OpenVINO), and containerized environments.
• Experience with edge and embedded platforms (e.g., NVIDIA Jetson or similar) and real-time constraints.
• Familiarity with sensor integration, robotics, and simulation environments is an advantage.

Deployment and Integration

• Model serving and APIs (REST, gRPC), Docker, Kubernetes, and CI/CD pipelines.
• MLOps practices: experiment tracking, model versioning, monitoring, and retraining.
• Integration of AI components with existing software systems and enterprise architecture.

Appreciated Skills

• Data Science: statistical analysis, exploratory data analysis, hypothesis testing, and data visualization.
• Data Engineering: data pipelines, ETL/ELT, SQL and NoSQL databases, and big data tools such as Spark.
• Experience with data quality, labeling strategies, and synthetic data generation.

Qualifications

• Minimum: Bachelor's degree in Computer Science, Artificial Intelligence, or a related field.
• Preferred: Master's or PhD specialized in AI, machine learning, or data science.
• Proven, documented experience designing, developing, and deploying advanced AI solutions in real-world use cases.
• Demonstrated track record of taking AI projects from PoC to production.
• Strong problem-solving, communication, and technical leadership skills.
• Publications, patents, or open-source contributions are a plus.

What We Offer

• Competitive compensation and benefits
• Opportunity to work on impactful, cutting-edge AI projects
• Professional development and career growth