GenAI Engineer

Capgemini; New York (NY); On-site; USD 140,000 - 200,000; Full time

Job summary

Capgemini is seeking a talented Generative AI Engineer to design, develop, and deploy AI-powered applications using LLMs, RAG, AI agents, and modern AI frameworks. The role collaborates with architects, data scientists, and product teams to build scalable GenAI solutions addressing real business needs.

The candidate will work across AI frameworks, vector databases, and cloud platforms to deliver end-to-end AI workloads, with a focus on responsible AI, performance monitoring, and cost

Qualifications

• 3-8 years of software development experience.
• 1+ years of hands-on experience with Generative AI or LLM-based solutions.
• Experience building AI-powered applications in production environments.

Responsibilities

• Develop GenAI solutions using LLMs, RAG, and AI agents.
• Design prompt engineering and optimization strategies.
• Deploy AI apps on Azure, AWS, or GCP with scalable architectures.
• Develop RESTful APIs and microservices for AI workloads.
• Collaborate with frontend and backend teams to deliver end-to-end GenAI solutions.

Skills

Python

Generative AI

LLMs

APIs

Education

Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, or related field

Tools

Docker

GitHub Actions

Terraform

Job description

We are looking for a talented Generative AI Engineer to design, develop, and deploy AI-powered applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and modern AI frameworks. The candidate will work closely with architects, data scientists, and product teams to build scalable GenAI solutions that solve real business problems.

Key Responsibilities

Generative AI Development

• Develop and deploy applications leveraging LLMs such as GPT-4, Claude, Gemini, Llama, and Mistral.
• Build conversational AI assistants, enterprise chatbots, and AI copilots.
• Design and implement prompt engineering and prompt optimization strategies.
• Fine-tune and evaluate foundation models where applicable.
• Develop AI workflows using Agentic AI frameworks.

RAG & Knowledge Systems

• Build and maintain Retrieval-Augmented Generation (RAG) solutions.
• Develop document ingestion, chunking, embedding, and retrieval pipelines.
• Integrate vector databases such as Pinecone, Weaviate, ChromaDB, Azure AI Search, or Qdrant.
• Optimize search relevance, grounding, and response quality.

AI Framework Development

• Develop applications using:
• LangChain
• Semantic Kernel
• LangGraph
• CrewAI
• AutoGen
• Create reusable AI components and accelerator frameworks.

API & Application Integration

• Integrate GenAI services with enterprise applications.
• Develop RESTful APIs and microservices for AI workloads.
  
• Collaborate with frontend and backend teams to deliver end-to-end AI solutions.
• Implement AI model monitoring and feedback mechanisms.
• Deploy AI applications on Azure, AWS, or GCP.
• Build CI/CD pipelines for GenAI solutions.
• Use Docker and Kubernetes for containerization and orchestration.
• Monitor and optimize costs, latency, and performance.

Security & Responsible AI

• Implement responsible AI practices, content filtering, and model guardrails.
• Ensure compliance with data privacy, security, and governance standards.
• Address hallucination, bias, and prompt injection risks.

Required Qualifications

Education

• Bachelor's or Master's degree in Computer Science, Information Technology, Artificial Intelligence, or a related field.

Experience

• 3-8 years of software development experience.
• 1+ years of hands-on experience with Generative AI or LLM-based solutions.
• Experience building AI-powered applications in production environments.

Technical Skills

Programming Languages

• Python (Mandatory)
• SQL

Generative AI & LLMs

• Anthropic Claude
• Google Gemini

AI Frameworks

• LangChain
• Semantic Kernel
• LangGraph
• CrewAI

Vector Databases

• Pinecone
• ChromaDB
• Weaviate
• Qdrant
• AWS

DevOps Tools

• Docker
• GitHub Actions
• Terraform

Preferred Qualifications

• Experience with Agentic AI and Multi-Agent Systems.
• Knowledge of MLOps and LLMOps practices.
• Experience with model evaluation frameworks such as RAGAS, TruLens, DeepEval, or Promptfoo.
• Azure AI Engineer (AI-102), AWS AI Practitioner, or equivalent certifications.

Soft Skills

• Strong analytical and problem-solving abilities.
• Excellent communication and stakeholder management skills.
• Ability to work in Agile/Scrum environments.
• Strong collaboration and learning mindset.

Nice to Have

• Experience building AI copilots for enterprise applications.
• Exposure to Azure AI Foundry, AWS Bedrock, or Vertex AI.
• Experience with OCR, Document Intelligence, and Knowledge Management systems.
• Understanding of AI governance and Responsible AI frameworks