We are seeking a Senior AI/ML Engineer to join our AI Services team. In this hands-on role, you will design, build, and operate AI-powered microservices that provide core business capabilities including OCR/IDP, NLP, computer vision, embeddings, and LLM services. Your work will focus on delivering production-grade AI services from prototype to deployment, ensuring scalability, reliability, and cost efficiency.
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Work Conditions:
- On-site
- Full-time
- 1-year contract (renewable annually)
Key Responsibilities:
- Design, implement, and maintain AI microservices (Python/FastAPI preferred) with strong APIs and backward-compatible versioning.
- Productionize ML/AI models (training, fine-tuning, evaluation, optimization) using ONNX Runtime, TensorRT, Triton Inference Server, and vLLM.
- Build and maintain MLOps/LLMOps pipelines with CI/CD, model registry, and data/versioning (DVC/LakeFS).
- Develop services for OCR/IDP, NLP, CV, embeddings, and LLM inference with a focus on low latency and high throughput.
- Implement observability and reliability features such as tracing, metrics, logging, and automated rollback.
- Optimize infrastructure for performance and cost efficiency: autoscaling, batching, caching, and GPU/CPU right-sizing.
- Collaborate with the AI Lead, data engineers, and platform teams to integrate services into enterprise systems.
- Write robust tests (unit, integration, performance) and clear technical documentation.
Must Have Qualifications:
- Bachelor’s or master’s degree in computer science, AI/ML, or a related field.
- 4–6+ years of total software/ML engineering experience, with at least 2 years of building production AI services.
- Strong proficiency in Python and microservice frameworks (FastAPI/Flask).
- Experience with Docker, Kubernetes, Helm, GitHub Actions/Azure DevOps.
- Hands-on with PyTorch, Hugging Face, OpenCV, and OCR tools (PaddleOCR/Tesseract).
- Practical knowledge of inference optimization (ONNX, TensorRT, quantization, batching).
- Familiarity with messaging systems (Kafka/RabbitMQ), caching solutions (Redis), and databases (PostgreSQL, vector DBs like FAISS, Milvus, pgvector).
- Cloud-native deployment experience (Azure preferred; AWS/GCP acceptable).
- Strong software engineering fundamentals (API design, testing, CI/CD).
Nice to Have:
- Experience with C#/.NET for enterprise microservices and API integration.
- Exposure to RAG architectures, retrieval evaluation, and safety/guardrail techniques.
- Knowledge of event-driven architectures, API gateways, and service mesh (Istio/Linkerd).
- Familiarity with data engineering tools (Airflow/Prefect, Delta/Lakehouse) and feature stores (Feast).
- Experience with Arabic NLP and multilingual systems.
Language Requirements:
Experience with Arabic NLP is a plus.
