Position Overview We are seeking a technically strong and solution-oriented Lead Applied AI Engineer to support the design and implementation of advanced AI and analytics solutions. This role is ideal for someone with 6+ years of experience in data science and applied machine learning who enjoys combining technical depth with real-world problem-solving. You will work closely with internal technical teams and external clients to translate business needs into scalable AI solutions. You ll also guide junior team members, contribute to hands-on model development, and ensure seamless delivery of analytical components into production environments.

Key Responsibilities

Agentic AI, LLMs & Modern AI Systems

• Architect and implement production-grade agentic AI systems using LLM orchestration frameworks and protocols (e.g., LangChain, LangGraph, Langfuse, MCP).
• Design and deploy Retrieval-Augmented Generation (RAG) pipelines including chunking strategies, vector store selection, hybrid retrieval, and evaluation.
• Build multi-agent systems with tool use, memory, planning, and inter-agent coordination for enterprise automation and decision-support.
• Evaluate and integrate frontier LLMs (OpenAI, Anthropic, Mistral, open-source) into secure, scalable production architectures, on cloud and on-prem.
• Stay at the forefront of emerging agentic AI research and translate findings into practical product capabilities.

Classical ML, Data Science & Analytics

• Design and implement end-to-end ML pipelines: data ingestion, feature engineering, model training, hyper parameter tuning, validation, and deployment.
• Apply classical ML techniques across regression, classification, clustering, time-series forecasting, anomaly detection, and optimization.
• Deliver rigorous data analysis and statistical modeling to generate actionable insights for clients.
• Ensure models are production-ready: robust, interpretable, monitored, and aligned with business KPIs.

Technical Leadership & Mentorship

• Lead, mentor, and coach junior data and AI scientists guiding their technical growth, reviewing their code, and building their problem-solving capabilities.
• Define and enforce technical standards, best practices, and review processes across the data science team.
• Drive knowledge-sharing through internal presentations, documentation, and technical sessions.

Client-Facing Analytics Solutioning

• Collaborate with client engagement and technical teams to understand business requirements and translate them into actionable AI/analytics solutions.
• Provide strategic input on solution design, aligning analytical capabilities with client objectives.
• Serve as a technical lead in solution delivery discussions and workshops with clients.

Integration, Deployment & MLOps

• Collaborate with engineering teams to ensure seamless deployment of models into production via APIs, containerization (Docker/Kubernetes), and CI/CD pipelines.
• Implement model monitoring, drift detection, and retraining workflows to maintain solution performance post-deployment.
• Champion clean, maintainable, well-documented code practices across the team.

Technical Stack

• Languages & Libraries: Python (primary), SQL, NumPy, pandas, scikit-learn, XGBoost, LightGBM
• Agentic & LLM Frameworks: LangChain, LangGraph, Model Context Protocol (MCP), OpenAI API, Anthropic API
• RAG & Vector Stores: FAISS, Pinecone; embedding models and evaluation frameworks
• Deep Learning: PyTorch, TensorFlow/Keras, Hugging Face Transformers
• Cloud: one or more of: AWS, GCP, Azure
• Databases: PostgreSQL, Oracle; vector databases
• MLOps: Docker, MLflow, CI/CD pipelines

Desired Candidate Profile

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• 6-8+ years of hands-on experience in data science, applied machine learning, and AI engineering.
• Demonstrable production experience with LLMs, RAG pipelines, and agentic AI systems.
• Strong classical ML and statistical modeling expertise across multiple problem types and domains.
• Demonstrated ability to lead technical work and mentor junior team members.
• Ability to balance technical depth with practical delivery and business impact.
• Excellent collaboration and communication skills to work across teams and with clients.
• Fluency in English (written and spoken).
• Arabic
• Nice to Have: Experience delivering AI solutions across multiple industry sectors (e.g., government, energy, finance, healthcare, or retail).
• Client-facing or consulting experience working directly with external stakeholders on solution design and delivery.
• Experience with on-premise LLM deployment and air-gapped AI environments.
• Familiarity with Arabic NLP and multilingual models.
• Background in decision intelligence, operations research, or simulation modeling.
• Publications, open-source contributions, or public technical presence.