Senior Data Scientist Engineer

Capgemini•Atlanta, GA

2d•$103,330 - $128,656•Hybrid

About The Position

Capgemini is hiring a Senior Data Scientist Engineer to design, build, deploy, and support production AI solutions, with expertise in conversational AI, large language models (LLMs), natural language processing, and machine learning. The role includes building and training machine learning models, validating LLM solution architectures, and implementing generative AI systems using tools and frameworks such as LangChain, TensorFlow, and PyTorch. The position also involves putting models into production, managing their lifecycle, and using cloud platforms to deploy and scale them. The engineer collaborates with data scientists, product managers, stakeholders, and cross-functional teams to align AI solutions with business objectives. They optimize model performance using statistical analysis and large datasets, automate workflows and improve decision-making with AI-driven solutions, troubleshoot and improve AI applications, and develop prototypes for testing and feedback before full-scale deployment. The role is based in Atlanta, Georgia, with day-one onsite work in a hybrid arrangement. Capgemini describes itself as a global business and technology transformation partner with 340,000 team members in more than 50 countries.

Requirements

At least 9 years of hands-on experience in AI development, data analysis, and machine learning techniques.
Experience putting machine learning models into production.
Experience using MLflow or similar tools for machine learning model lifecycle management.
Experience implementing LLMs using vector databases and Retrieval-Augmented Generation (RAG), and tuning models.
Experience using GPT, Llama, or another LLM.
Experience putting generative AI models into production and supporting them.
Knowledge of machine learning models and LLMs, using LangChain or a similar tool.
Familiarity with AWS, Google Cloud, or Azure for model deployment and scaling.
Experience or ability to use TensorFlow and PyTorch to create models.

Responsibilities

Design and develop AI systems, algorithms, and machine learning models to solve challenges across industries.
Build and train machine learning models, including models for tasks such as NLP, computer vision, and robotics.
Train, build, deploy, and support production AI and generative AI solutions.
Validate solution architectures for LLMs.
Implement LLM solutions using vector databases and Retrieval-Augmented Generation (RAG), and tune models.
Use cloud computing platforms to deploy and scale models.
Write clean, efficient, and reusable code for MLOps work.
Collaborate with cross-functional teams, data scientists, product managers, and stakeholders to align AI solutions with business objectives.
Optimize AI model performance using statistical analysis and large datasets, and make adjustments to ensure efficient operation.
Develop AI-driven solutions to automate workflows and improve decision-making.
Stay current with AI technologies and frameworks to enhance system functionality and scalability.
Troubleshoot and improve AI applications to resolve issues and optimize performance and user experience.
Develop AI prototypes for testing and feedback before full-scale deployment.
Use MLflow or similar tools for lifecycle management of machine learning models.

Benefits

Flexible work
Healthcare, including dental and vision coverage, mental health, and well-being programs
Financial well-being programs, including 401(k) and Employee Share Ownership Plan
Paid time off and paid holidays
Paid parental leave
Family-building benefits, including adoption assistance, surrogacy, and cryopreservation
Subsidized backup child and elder care and tutoring
Mentoring, coaching, and learning programs
Employee Resource Groups
Disaster relief
Life and disability insurance
Employee assistance programs