{"id":1805813,"url":"https://alion.io/job/capgemini-gen-aiagentic-ai-full-stack-engineer","title":"Gen AI/Agentic AI Full stack Engineer","company":{"id":145,"name":"Capgemini","domain":"capgemini.com","url":"https://alion.io/company/capgemini","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Career site","truth_index":{"grade":"B","score":75,"open_postings":915,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":23,"computed_at":"2026-10-05T05:45:15Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cairo, Egypt"],"countries":["EG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":55000,"max_usd":164000,"period":"year","method":null,"sample_n":5136},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"AWS Bedrock","optional":false},{"name":"Azure","optional":false},{"name":"Azure Cosmos DB","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"CrewAI","optional":false},{"name":"DeepEval","optional":false},{"name":"Django","optional":false},{"name":"DynamoDB","optional":false},{"name":"FAISS","optional":false},{"name":"FastAPI","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Jenkins","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-06-08T06:30:50Z","employer_posted_date":"2026-09-24","last_verified_at":"2026-10-06T02:05:51Z","board_verified":true,"closed_at":null,"days_open":119,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":118},"description":"Long Description\nJob Description\nWe are seeking a Senior Gen AI/Agentic AI Full stack with Generative AI, Agentic Frameworks, Machine Learning (ML), AI and Full stack Python with strong knowledge on Cloud technologies like AWS, Azure, GCP. Ideal candidate is expected to have prior experience in end-to-end implementation of Gen AI solutions, Machine Learning models that includes identification of ‘right’ problem, designing ‘optimum’ solution, implementing using ‘best in class’ practices and deploying the models to production. Will work in alignment with data strategy at various clients, using multiple technologies and platforms.\nResponsibilities\nCollaborate with data engineers, data scientists, and business stakeholders to understand the data and the business problems.\nProven hands-on Gen AI / Agentic AI and data science with machine learning\nStrong knowledge of Python\nExperience of deploying the Gen AI applications with one of the Agent Frameworks like Langgraph, Autogen, Crew AI.\nExperience in deploying the Gen AI stack/services provided by various platforms such as AWS, GCP, Azure\nExperience in Generative AI and working with multiple Large Language Models and implementing Advanced RAG based solutions.\nExperience in processing/ingesting unstructured data from PDFs, HTML, Image files, audio to text etc.\nExperience with data gathering, data quality, system architecture, coding best practices\nHands-on experience with Vector Databases (such as FAISS, Pinecone, Weaviate, or Azure AI Search).\nExperience with Lean / Agile development methodologies\nQualifications\nBachelor's degree in computer science, Engineering, Mathematics, Statistics, or related field\nProficient in Python and common machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, etc.\nHands-on experience with CI/CD pipelines and DevOps tools like Jenkins, GitHub Actions, or Terraform.\nProficiency in NoSQL and SQL databases (PostgreSQL, MongoDB, CosmosDB, DynamoDB).\nExperience in Python AI/ML frameworks such as TensorFlow, PyTorch, or LangChain.\nExperience in creating Web UI applications, ML models & pipelines, Gen AI and Agentic AI solutions.\nExperience in Agentic AI frameworks like Autogen, Crew AI, Semantic Kernal, Langgraph etc.,\nStrong understanding of LLM fine-tuning, local deployment of open source models\nProficiency in building RESTful APIs using FastAPI, Flask, or Django.\nKnowledge in Model evaluation tools like DeepEval, FMeval, RAGAS , Bedrock model 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