Lead Advanced Analytics Engineer (AI)

Lead Advanced Analytics Engineer (AI) | Fulltime

**Job Description:

Job Description:**

• Build and deploy production-grade Generative AI and Agentic AI solutions across the organization.
• Develop multi-agent systems and agent-to-agent (A2A) orchestration for autonomous, end-to-end workflows.
• Build RAG pipelines with vector databases and embeddings to ground LLMs in enterprise data.
• Design, train, and deploy Deep Learning models (NLP, CV, time-series) using PyTorch.
• Use AWS (Bedrock, SageMaker) and Azure (OpenAI, ML Studio) to host and scale AI workloads.
• Integrate Snowflake Cortex AI/ML, SAP Joule, and MuleSoft AI Chain with enterprise systems.
• Apply prompt engineering, function calling, and guardrails for reliable LLM apps.
• Operationalize models with MLOps/LLMOps: CI/CD, evaluation, monitoring, and responsible AI.

Key Responsibilities:

• Build LLM apps and agentic workflows using LangChain, LangGraph, LlamaIndex, AutoGen, or CrewAI.
• Design agent-to-agent (A2A) orchestration: planning, tool use, memory, and multi-agent collaboration.
• Develop RAG pipelines: chunking, embeddings, vector search, re-ranking, and grounding.
• Train and fine-tune deep learning models with PyTorch.
• Deploy models on AWS Bedrock, SageMaker, Azure OpenAI, and Azure ML Studio.
• Integrate AI with Snowflake Cortex, SAP Joule, and MuleSoft AI Chain.
• Apply prompt engineering, evaluation, and guardrails for safe, accurate outputs.
• Implement MLOps/LLMOps: experiment tracking, CI/CD, monitoring, and drift detection.
• Collaborate with engineering, data, and business teams and document architectures.

Deliverables:

• Production GenAI and agentic applications adopted by business users.
• Working multi-agent systems with A2A orchestration automating key workflows.
• Reliable RAG pipelines with accurate, grounded LLM responses.
• Trained and deployed deep learning models meeting accuracy, latency, and cost targets.
• Successful integrations with Snowflake Cortex, SAP Joule, and MuleSoft AI Chain.
• End-to-end MLOps/LLMOps pipelines for training, deployment, and monitoring.
• Responsible AI guardrails ensuring quality, safety, and governance.
• Reusable AI components, prompt libraries, and reference architectures.
• Clear technical documentation and architecture diagrams.
• Measurable business outcomes: efficiency, automation, and better user experience.

Job Requirements:

• 5+years of experience
• Bachelor's degree in Computer Science, AI, Data Science, Software Engineering, or Mathematics.
• Certifications in AWS, Azure, Snowflake, GenAI, or ML preferred.
• Equivalent hands-on AI/ML experience may substitute for certifications.

Mandatory Technical Skills:

• Strong Python with NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, Hugging Face.
• Hands-on with LLM and agent frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI.
• Experience designing agent-to-agent (A2A) orchestration: tool use, planning, function calling, memory.
• Practical RAG experience with vector DBs (Pinecone, Weaviate, Chroma, FAISS, pgvector).
• Solid Deep Learning: transformers, CNNs, RNNs, fine-tuning, and model evaluation.
• Hands-on with AWS AI/ML: Bedrock, SageMaker, Lambda, S3.
• Hands-on with Azure AI/ML: Azure OpenAI, ML Studio, AI Foundry.
• Good experience with Snowflake AI/ML: Cortex AI and Snowpark ML.
• Good experience with SAP Joule: GenAI copilot and SAP integrations.
• Good experience with MuleSoft AI Chain on Anypoint Platform.
• Proficient in prompt engineering, evaluation, and guardrails.
• Experience with REST APIs, Git, Docker, and CI/CD for models.
• Solid MLOps/LLMOps: tracking, versioning, monitoring, responsible AI.
• Strong analytical, problem-solving, and communication skills.

Tools and Technologies:

• Languages & Frameworks: Python, PyTorch, TensorFlow, Hugging Face, scikit-learn, FastAPI.
• LLM & Agent Frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, Semantic Kernel.
• LLM Providers: OpenAI, Anthropic Claude, Llama, Mistral, Cohere, Hugging Face.
• Cloud AI: AWS Bedrock, SageMaker, Azure OpenAI, Azure ML Studio, AI Foundry.
• Enterprise AI: Snowflake Cortex & Snowpark ML, SAP Joule, MuleSoft AI Chain.
• Vector DBs: Pinecone, Weaviate, Chroma, FAISS, pgvector, Milvus.
• Data & Storage: Snowflake, SQL, PostgreSQL, MongoDB, S3, Azure Data Lake, Pandas, Spark.
• MLOps/LLMOps: MLflow, Weights & Biases, LangSmith, LangFuse, Docker, Kubernetes, Git CI/CD.
• Evaluation: RAGAS, DeepEval, TruLens, Arize.
• Collaboration: Jupyter, VS Code, Jira, Confluence, Postman.

Equal opportunity:

All qualified applicants will receive consideration for employment without regard to age, religion, gender, nationality or disability. All qualified candidates will be considered in the process

Posted Today

• Job Location

• Egypt

• Job Code

• 2145

Job Overview

• Experience

• 5+ Years

• Job Level:

• Mid Career

• Education

• Bachelor's degree in computer science or equivalent