AI Engineer

This AI Engineer position at Capgemini in Atlanta, GA focuses on designing and deploying production AI solutions, including conversational AI, LLM-based systems, NLP, and machine learning. The role includes cloud deployment, MLOps support, and ongoing optimization and troubleshooting of AI applications.

Responsibilities

• Design and develop AI systems to address specific challenges across industries.
• Build algorithms and machine learning models, including training and deployment support for NLP, computer vision, and robotics use cases.
• Collaborate with cross-functional teams, including data scientists, product managers, and stakeholders, to align AI delivery with business objectives.
• Optimize model and application performance using statistical analysis and large datasets.
• Implement AI-driven automation to streamline workflows and improve decision-making.
• Stay current with emerging AI technologies and frameworks to improve functionality and scalability.
• Troubleshoot and improve AI applications to enhance performance and user experience.
• Develop AI prototypes to test initial approaches and incorporate feedback prior to full-scale deployment.

Requirements

• Expertise in AI, including conversational AI, LLMs, NLP, and machine learning, with experience training, building, and deploying production AI solutions.
• 5+ years of hands-on experience in AI development, data analysis, and machine learning techniques, including experience putting machine learning models into production.
• Experience with Mlflow or similar tools for machine learning lifecycle management.
• Advanced knowledge of machine learning models and large language models, including use of langchain or a comparable tool.
• Experience implementing LLMs using vector-based approaches and Retrieval-Augmented Generation (RAG), including model tuning.
• Experience with GPTs, Llama, or other LLMs.
• Ability to perform solution architecture validations for LLM systems.
• Experience putting generative AI (GENAI) models into production and providing ongoing support.
• Familiarity with AWS, Google Cloud, or Azure for deployment and scaling of models.
• Ability to write clean, efficient, and reusable code for MLOps roles.

Technologies

• Mlflow
• langchain
• Retrieval-Augmented Generation (RAG)
• GPTs
• Llama
• AWS
• Google Cloud
• Azure
• TensorFlow
• PyTorch

Compensation

Base compensation range (posted location): $90,000 to $96,665 per year.

Benefits

• Paid time off based on employee grade (A-F) defined by 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 (for example, 401(k) in the U.S., RRSP in Canada).
• Life and disability insurance.
• Employee assistance programs.