29 days ago
Cairo, EgyptSenior
Responsibilities
- Design, develop, and productionize agentic AI applications and AI-driven workflows using LLMs.
- Build single-agent and multi-agent solutions with orchestration, reasoning, planning, memory, tool usage, RAG, embeddings, vector search, and knowledge bases.
- Implement LLM tool and function calling and integrate agents with APIs, databases, enterprise applications, and external services.
- Develop scalable Python backend components and APIs using modern software engineering practices.
- Implement security, guardrails, evaluation, monitoring, observability, and Responsible AI practices.
- Design evaluations for agent quality, accuracy, reliability, performance, and cost.
- Deploy and operate AI solutions in cloud and containerized environments.
- Collaborate with architects, data engineers, product owners, and business stakeholders to translate use cases into scalable solutions.
- Own significant technical components, contribute to engineering decisions and standards, and mentor junior engineers.
Requirements
- At least 5 years of professional experience in software engineering, AI engineering, data engineering, or related technical roles.
- Strong hands-on Python development skills and practical experience building generative AI, LLM, and agentic AI applications.
- Experience with agent orchestration, tool or function calling, RAG architectures, embeddings, vector search, context and memory management, and enterprise system integration.
- Experience with at least one agentic AI or generative AI framework such as LangGraph, LangChain, Microsoft Semantic Kernel, or AutoGen.
- Strong software engineering fundamentals, including API development, Git, testing, CI/CD, and production-quality coding.
- Experience with Docker and/or Kubernetes and at least one major cloud platform: Azure, AWS, or GCP.
- Strong analytical, troubleshooting, problem-solving, independent ownership, and collaboration skills.
- Data engineering experience with data pipelines, ETL/ELT, data integration, SQL, databases, data lakes, lakehouses, warehouses, or modern data platforms is strongly advantageous.
- Additional desirable experience includes Databricks, Spark, Snowflake, Microsoft Fabric, Azure Data Factory, AWS Glue, enterprise GenAI platforms, vector databases, MLOps/LLMOps, evaluation and observability, cloud-native architectures, microservices, Responsible AI, security, content safety, AI governance, and large-enterprise AI delivery.
Tech Stack
Categories
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.