Conversational AI Engineer

Capgemini; Atlanta (GA); On-site; USD 80,000 - 90,000; Full time

Benefits offered by this job

12-25 days Vacation

Company paid holidays

Personal Days

Sick Leave

Medical, dental, and vision coverage

401(k) in the US

RRSP in Canada

Life and disability insurance

Employee assistance programs

Job summary

Capgemini is seeking a Conversational AI Engineer in Atlanta, GA, for an onsite role focused on building production-ready conversational AI with LLMs and cloud deployment.

You will design, develop, and deploy AI solutions using GPT, Llama, and other foundation models, build enterprise chatbots, implement RAG architectures, and optimize prompts for better UX. Strong Python, Angular, and ML lifecycle management skills are required.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, ML, Data Science, or related field.
• 5+ years of hands-on experience in AI development, data analysis, and machine learning.
• Proven experience building and deploying production-grade AI/ML solutions.
• Strong expertise in Conversational AI, Generative AI, NLP, and LLMs.
• Experience with LangChain or similar LLM orchestration frameworks.
• Hands-on experience implementing RAG solutions using vector databases.
• Experience with GPT, Llama, or other LLMs.
• Strong Python and software engineering skills.
• Experience with Angular and modern web app development.
• Knowledge of MLflow or similar MLOps platforms.
• Experience with TensorFlow, PyTorch, or similar frameworks.
• Familiarity with Azure, AWS, or Google Cloud for AI deployment and scaling.

Responsibilities

• Design, develop, and deploy conversational AI solutions using Large Language Models such as GPT, Llama, and other foundation models.
• Build intelligent chatbots, virtual assistants, and AI-powered applications for enterprise use cases.
• Implement RAG architectures with vector databases and semantic search.
• Create and optimize prompt engineering strategies to improve response quality and user experience.
• Evaluate, fine-tune, and optimize LLMs for business-specific applications.
• Apply NLP techniques including intent recognition, text classification, sentiment analysis, entity extraction, summarization, and semantic search.
• Use frameworks such as TensorFlow, PyTorch, and other AI/ML platforms to deliver scalable solutions.
• Own the end-to-end AI and ML lifecycle using MLflow or similar MLOps tools.
• Deploy, monitor, and maintain production-grade AI/GenAI applications.
• Ensure reliability, scalability, and operational excellence for deployed AI systems.
• Integrate conversational AI capabilities into modern web apps using Angular.
• Develop responsive user interfaces for AI-powered experiences.
• Collaborate with backend and AI teams to connect LLM/ML services with enterprise applications.
• Implement real-time conversational experiences and intelligent user interactions.
• Validate and review LLM solution architectures to support performance, governance, and scalability.
• Deploy AI/ML models on cloud platforms including Azure, AWS, or Google Cloud Platform.
• Stay current with advancements in Generative AI, Conversational AI, LLMs, NLP, and emerging AI technologies.
• Develop proof-of-concepts, prototypes, and pilot solutions to evaluate new technologies.

Skills

Conversational AI

NLP

LLMs

Python

Angular

LangChain

MLOps

TensorFlow

PyTorch

Cloud deployment

Education

Bachelor's or Master's degree in Computer Science / AI / ML / Data Science

Tools

MLflow

Job description

Capgemini is hiring a Conversational AI Engineer in Atlanta, GA for an onsite role focused on building production-ready conversational AI with LLMs and cloud deployment.

Responsibilities

• Design, develop, and deploy conversational AI solutions using Large Language Models such as GPT, Llama, and other foundation models
• Build intelligent chatbots, virtual assistants, and AI-powered applications for enterprise use cases
• Implement Retrieval-Augmented Generation (RAG) architectures with vector databases and semantic search
• Create and optimize prompt engineering strategies to improve response quality, accuracy, and user experience
• Evaluate, fine-tune, and optimize LLMs for business-specific applications
• Apply NLP techniques including intent recognition, text classification, sentiment analysis, entity extraction, summarization, and semantic search
• Use frameworks such as TensorFlow, PyTorch, and other AI/ML platforms to deliver scalable solutions
• Analyze large datasets and improve model performance through experimentation and optimization
• Own the end-to-end AI and ML lifecycle using MLflow or similar MLOps tools
• Deploy, monitor, and maintain production-grade AI/GenAI applications
• Ensure reliability, scalability, and operational excellence for deployed AI systems
• Integrate conversational AI capabilities into modern web apps using Angular
• Develop responsive user interfaces for AI-powered experiences
• Collaborate with backend and AI teams to connect LLM/ML services with enterprise applications
• Implement real-time conversational experiences and intelligent user interactions
• Validate and review LLM solution architectures to support performance, governance, and scalability
• Deploy AI/ML models on cloud platforms including Azure, AWS, or Google Cloud Platform
• Stay current with advancements in Generative AI, Conversational AI, LLMs, NLP, and emerging AI technologies
• Develop proof-of-concepts, prototypes, and pilot solutions to evaluate new technologies

Requirements

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or related field
• 5+ years of hands-on experience in AI development, data analysis, and machine learning
• Proven experience building and deploying production-grade AI/ML solutions
• Strong expertise in Conversational AI, Generative AI, NLP, and Large Language Models (LLMs)
• Experience with LangChain or similar LLM orchestration frameworks
• Hands‑on experience implementing RAG solutions using vector databases
• Experience with GPT, Llama, or other commercial/open‑source LLMs
• Strong proficiency in Python and software engineering best practices
• Experience with Angular and modern web application development
• Knowledge of MLflow or similar MLOps platforms for model lifecycle management
• Experience with TensorFlow, PyTorch, or equivalent machine learning frameworks
• Familiarity with Azure, AWS, or Google Cloud services for AI deployment and scaling

Technologies

• Large Language Models (LLMs), GPT, Llama
• Natural Language Processing (NLP), Machine Learning, Generative AI
• Retrieval-Augmented Generation (RAG), vector databases, semantic search technologies
• TensorFlow, PyTorch, MLflow
• LangChain, Python, Angular
• Azure, AWS, Google Cloud Platform

Compensation

• USD 80,000 - 90,000 per year

Benefits

• Paid time off based on employee grade (A-F) per policy: Vacation 12-25 days depending on grade, Company paid holidays, Personal Days, Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility