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# AI Engineer

- **Company:** Capgemini
- **Location:** Roanoke, TX, United States
- **Experience:** Expert
- **Salary:** $90,000.0 - $96,665.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Vision, Microsoft Azure, Big Data, Cloud Computing, Code Reuse, Machine Learning, Natural Language Processing, Tensorflow, Software Engineering, Google Cloud, Cloud Platform System, Pytorch, Large Language Models, Generative AI, Machine Learning Operations
- **Published:** October 16, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3451605160&tx=DT10394UYZ&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

Key Skills - Expertise in AI (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 and experience putting Machine Learning models into production.

MLOps tools: Experience using Mlflow or similar tools for lifecycle management of machine learning models.

Machine Learning Models: Advanced knowledge in Machine Learning models and Large Language Models (LLM), using langchain or a similar tool.

LLM Implementation: Experience in implementing LLMs using vector bases and Retrieval-Augmented Generation (RAG), as well as tuning models. Using GPTs, Llama, or any other LLM

Solution Architecture Validation: Ability to perform solution architecture validations for LLMs. GenAIOps: Experience in putting Generative AI (GENAI) models into production and providing support to them.

Experience with Cloud Computing: Familiarity with cloud computing platforms such as AWS, Google Cloud, or Azure for model deployment and scaling.

Software Engineering Skills: The ability to write clean, efficient, and reusable code is essential in MLOps roles.

Designs and develops AI systems. Builds algorithms and machine learning models to solve specific challenges across industries.

## Description

Collaborates with cross-functional teams. Works with data scientists, product managers, and stakeholders to align AI solutions with business objectives.

Builds and trains machine learning models. Utilizes tools like TensorFlow and PyTorch to create models for tasks such as NLP, computer vision, and robotics.

Optimizes performance. Ensures AI models operate efficiently, making adjustments based on statistical analysis and large data sets.

Implements automation. Develops AI-driven solutions to streamline workflows and improve decision-making.

Explores advancements in AI. Stays up to date with the latest technologies and frameworks to enhance the functionality and scalability of AI systems.

Troubleshoots and improves AI applications. Resolves issues and optimizes systems for better performance and user experience.

Develops AI prototypes. Builds initial versions of AI applications for testing and feedback before full-scale deployment.