Develop and deploy end-to-end AI/ML solutions using Python and LLM/GenAI frameworks and tools
Design, implement, and maintain CI/CD pipelines, containerize LLM models, and deploy them on cloud or on-premise environments
Conduct thorough testing, deployment, and ongoing maintenance of models to ensure optimal performance throughout their lifecycle
Research, design, and train innovative applications leveraging LLMs to solve complex real-world problems
Build prototypes and proofs-of-concept (PoCs) to demonstrate solution feasibility and value, and provide robust architecture solutions
Design and implement Retrieval-Augmented Generation (RAG) pipelines for specific use cases
Develop and optimize LLM embeddings for diverse applications, including custom training pipelines
Provide technical guidance to clients adopting LLM technologies, ensuring seamless integration and effective use
Collaborate with teams to ensure compliance with Responsible AI standards and protocols
Required Qualifications
Education: Bachelor's degree in Statistics, Applied Mathematics, Computer Science, or related fields (final-year students may be considered)
Experience:
3+ years of hands-on experience in AI/ML technologies and software engineering
2+ years of experience in shell scripting and NLP, including at least 1+ year of experience working with LLM/GenAI technologies like OpenAI API, ChatGPT, GPT-4, LangChain, HuggingFace Transformers, or similar
1+ year of experience with prompt engineering and vector databases (e.g., pgvector)
2+ years of experience with AWS, GCP, or Microsoft Azure
2+ years of experience with MLOps, CI/CD pipeline development, containerization, and deploying models in production
Strong proficiency in Python, including experience with AI and ML libraries such as PyTorch and TensorFlow
Proven experience fine-tuning and deploying LLMs, as well as implementing RAG architectures
Excellent problem-solving skills, with the ability to optimize model performance
Fluency in both Arabic and English, with the ability to communicate complex technical concepts to non-technical stakeholders
Desired Qualifications
In-depth expertise in a specific domain or industry, particularly in NLP/LLM applications
Applied research experience in developing production-grade LLM solutions
Knowledge of Responsible AI standards and their implementation
Familiarity with MLflow or other experiment-tracking tools
Experience with cloud platforms such as GCP for AI/ML workloads
Required Skills
Proficiency in Python programming and AI/ML frameworks
Hands-on experience in fine-tuning, deploying, and maintaining LLMs
Solid understanding of RAG pipelines, embeddings, and vector databases
Expertise in MLOps and best practices for model deployment
Automated testing experience to ensure robust pipelines
