GenAI & RAG Engineer – Agentic AI Specialist (Hybrid)

Capgemini; Charlotte (NC); Hybrid; USD 150,000 - 210,000; Full time

Benefits offered by this job

Healthcare benefits

Paid time off

Parental leave

Backup care programs

Job summary

Capgemini is seeking a GenAI/ML Engineer with hands-on RAG and Agentic AI experience to craft scalable AI solutions. You will design, develop, and deploy advanced systems using LLMs, orchestration frameworks, and robust ML pipelines.

The role emphasizes integration with enterprise data sources, rapid prototyping, and production-grade deployment across cloud platforms. Strong collaboration with business teams is essential.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field.
• 5+ years of experience in Machine Learning / AI development.
• Strong programming in Python.
• Hands‑on experience with LLMs and GenAI frameworks.

Responsibilities

• Design and implement GenAI solutions using LLMs (GPT, Claude, or open models).
• Build and optimize RAG pipelines with vector databases.
• Develop Agentic AI systems for planning, reasoning, and multi-step tasks.
• Integrate LLMs with enterprise data sources, APIs and tools.
• Fine-tune, evaluate, and monitor model performance for accuracy, latency, and cost.

Skills

GenAI

LLMs

RAG

Agentic AI

Python

LangChain

LlamaIndex

Pinecone

FAISS

Weaviate

Chroma

PyTorch

TensorFlow

Hugging Face

APIs

REST

Cloud (AWS Azure GCP)

ML Pipelines

Docker

Kubernetes

Education

Bachelor’s or Master’s degree in Computer Science/AI/Data Science

Tools

Pinecone

FAISS

Weaviate

Chroma

LangChain

LlamaIndex

Semantic Kernel

Docker

Kubernetes

REST APIs

PyTorch

TensorFlow

Hugging Face

Job description

Capgemini is seeking a GenAI/ML Engineer with hands-on RAG and Agentic AI experience to craft scalable AI solutions. You will design, develop, and deploy advanced systems using LLMs, orchestration frameworks, and robust ML pipelines.

The role emphasizes integration with enterprise data sources, rapid prototyping, and production-grade deployment across cloud platforms. Strong collaboration with business teams is essential.