Senior Data Scientist Engineer

Capgemini • Atlanta, GA

8h • $103,330 - $128,656 • Hybrid

About The Position

Capgemini is hiring a Senior Data Scientist Engineer to develop, deploy, and support production AI solutions, with a focus on conversational AI, large language models (LLMs), natural language processing, and machine learning. The role is based in Atlanta, Georgia, and is described as day-one onsite with a hybrid work arrangement.

The position involves building and training machine learning models, validating LLM solution architectures, optimizing model performance, and developing AI-driven solutions and prototypes. The engineer will collaborate with data scientists, product managers, and other stakeholders to align solutions with business objectives. The role also involves troubleshooting AI applications, improving their performance and user experience, and keeping up with advances in AI technologies and frameworks. Cloud platforms may be used to deploy and scale models, and the job emphasizes clean, efficient, reusable code and MLOps practices.

The position requires at least nine years of hands-on experience in AI development, data analysis, and machine learning, including putting machine learning and generative AI models into production and supporting them. Relevant work includes using MLflow or similar lifecycle-management tools, implementing LLMs with vector databases and Retrieval-Augmented Generation (RAG), tuning models, and working with GPT, Llama, or other LLMs. The description also references TensorFlow and PyTorch for creating models for tasks such as NLP, computer vision, and robotics.

Capgemini describes a broad benefits package for eligible employees. Benefits and eligibility may vary by location and local policy. The base salary range listed for the posted location is $103,330 to $128,656 per year; the role may also be eligible for variable compensation, bonuses, or commissions.

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 with GPT, Llama, or another large language model.
• Experience putting generative AI models into production and supporting them.
• Advanced knowledge of machine learning models and large language models.
• Ability to perform solution architecture validations for LLMs.
• Ability to write clean, efficient, reusable code.
• Experience developing models with tools such as TensorFlow and PyTorch for tasks including NLP, computer vision, and robotics.

Nice To Haves

• Familiarity with cloud computing platforms such as AWS, Google Cloud, or Azure for model deployment and scaling.
• Experience with conversational AI and NLP.

Responsibilities

• Build and train machine learning models and develop algorithms to address specific challenges.
• Develop, deploy, and support production AI and generative AI solutions.
• Validate solution architectures for large language models.
• Optimize model performance using statistical analysis and large datasets, and make adjustments to improve efficiency.
• Implement AI-driven solutions to streamline workflows and improve decision-making.
• Develop AI prototypes for testing and feedback before full-scale deployment.
• Troubleshoot AI applications and optimize systems to improve performance and user experience.
• Collaborate with data scientists, product managers, and stakeholders to align AI solutions with business objectives.
• Use cloud computing platforms to deploy and scale models.
• Write clean, efficient, reusable code for MLOps work.
• Stay current with AI technologies and frameworks to improve AI system functionality and scalability.

Benefits

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed