Gen AI/ ML Engineer (RAG & Agentic AI)

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

Location : New York City, NY / Charlotte, NC (Hybrid/Onsite)

Experience : 8+ Years Preferred

Employee Type : Full Time with Benefits

In-Person Interview Location: NYC, NY/Atlanta, GA/ Chicago, IL/ Irving, TX/ Charlotte, NC

Job Description:

We are seeking a highly skilled GenAI / ML Engineer with hands-on experience in Retrieval-Augmented Generation (RAG) and Agentic AI systems to design, develop, and deploy next-generation AI solutions. The ideal candidate will have strong expertise in building intelligent systems using LLMs, orchestration frameworks, and scalable ML pipelines.

Key Responsibilities

• Design and implement GenAI solutions using Large Language Models (LLMs) such as GPT, Claude, or open-source models
• Build and optimize RAG pipelines leveraging vector databases (e.g., Pinecone, FAISS, Weaviate, Chroma)
• Develop Agentic AI systems capable of planning, reasoning, and multi-step task execution using frameworks like LangChain, LlamaIndex, Semantic Kernel, or similar
• Integrate LLMs with enterprise data sources, APIs, and tools for real-world use cases
• Fine-tune, evaluate, and monitor model performance for accuracy, latency, and cost efficiency
• Develop scalable and production-ready ML pipelines using cloud platforms (Azure, AWS, or GCP)
• Collaborate with cross-functional teams to translate business requirements into AI-driven solutions
• Implement best practices for prompt engineering, guardrails, and responsible AI
• Stay updated with latest advancements in GenAI, LLMOps, and agent-based architectures

Required Skills & 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 skills in Python
• Hands‑on experience with LLMs and GenAI frameworks
• Proven experience in building RAG-based applications
• Experience with Agentic AI / autonomous agents / multi‑agent systems
• Familiarity with vector databases and embeddings
• Experience with ML frameworks such as PyTorch, TensorFlow, or Hugging Face
• Exposure to API integrations, microservices architecture, and RESTful services
• Experience working with cloud platforms (AWS / Azure / GCP)

Preferred Qualifications

• Experience with LLMOps tools and model deployment frameworks
• Knowledge of fine‑tuning techniques (LoRA, PEFT, RLHF)
• Experience in knowledge graph integration
• Familiarity with CI/CD pipelines and containerization (Docker, Kubernetes)
• Prior experience in enterprise AI implementations
• Strong problem‑solving and analytical skills
• Excellent communication and collaboration abilities
• Ability to work in a fast‑paced and innovation‑driven environment

Life At Capgemini

Capgemini supports all aspects of your well‑being throughout the changing stages of your life and career. For eligible employees, we offer:

• Healthcare including dental, vision, mental health, and well‑being programs
• Financial well‑being programs such as 401(k) and Employee Share Ownership Plan
• Paid time off and paid holidays
• Paid parental leave
• Family building benefits like adoption assistance, surrogacy, and cryopreservation
• Social well‑being benefits like subsidized back‑up child/elder care and tutoring
• Mentoring, coaching and learning programs
• Employee Resource Groups
• Disaster Relief

Disclaimer

Capgemini is an Equal Opportunity Employer encouraging diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.

This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodations do not pose an undue hardship.

Capgemini is committed to providing reasonable accommodations during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.