Data Scientist
Capgemini
•
Nashville, TN
0
0
0
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Entry level
Posted March 20, 2026
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Responsibilities
Commitments
Responsibilities
- This role involves developing GEN AI solutions and predictive AI models, deploying them in production environments, and driving the integration of AI technologies across our business operations.
- As a key member of our AI team, you will collaborate with diverse teams to design solutions that deliver tangible business value through AI-driven insights.
- Key Responsibilities: Application Architecture Design, Development, & Integration: Familiarity with API architecture, and components such as external interfacing, traffic control, runtime execution of business logic, data access, authentication, deployment.
- Key skills to include Understanding of URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation.
- AI & Machine Learning Models Development: Develop generative and predictive AI models (including NLP, computer vision, etc.).Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and big data tools (e.g., Databricks, PySpark) to develop AI solutions.
- Familiarity with intelligent autonomous agents for complex tasks and multimodal interactions.
- Familiarity with agentic workflows that utilize AI agents to automate tasks and improve operational efficiency.
- Model Deployment & Maintenance: Deploy AI models into production environments, ensuring scalability, performance, and optimization.
- Monitor and troubleshoot deployed models and pipelines for optimal performance.
- Design and maintain data pipelines for efficient data collection, processing, and storage (e.g., data lakes, data warehouses).
- Emerging Technologies: maintain involvement with internal and external training and relevant discussions; stay at the forefront of emerging AI techniques, tools, and trends.
- Collaboration & Communication: Collaborate with cross-functional teams to align AI projects with business requirements and strategic goals.
- Willingness to contribute to and participate in developing and harvesting resuable assets and demos, sales pitches.
- Communicate complex AI concepts and results to non-technical stakeholders.
- Experience in model deployment, MLOps, model monitoring, and managing data/model drift.
Commitments
Willingness to contribute to and participate in developing and harvesting resuable assets and demos, sales pitches.
Not Met Priorities
What still needs stronger evidence
Requirements
- Key Responsibilities: Application Architecture Design, Development, & Integration: Familiarity with API architecture, and components such as external interfacing, traffic control, runtime execution of business logic, data access, authentication, deployment.
- Key skills to include Understanding of URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation.
- AI & Machine Learning Models Development: Develop generative and predictive AI models (including NLP, computer vision, etc.).Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and big data tools (e.g., Databricks, PySpark) to develop AI solutions.
- Familiarity with intelligent autonomous agents for complex tasks and multimodal interactions.
- Familiarity with agentic workflows that utilize AI agents to automate tasks and improve operational efficiency.
- Model Deployment & Maintenance: Deploy AI models into production environments, ensuring scalability, performance, and optimization.
- Monitor and troubleshoot deployed models and pipelines for optimal performance.
- Design and maintain data pipelines for efficient data collection, processing, and storage (e.g., data lakes, data warehouses).
- Emerging Technologies: maintain involvement with internal and external training and relevant discussions; stay at the forefront of emerging AI techniques, tools, and trends.
- Collaboration & Communication: Collaborate with cross-functional teams to align AI projects with business requirements and strategic goals.
- Willingness to contribute to and participate in developing and harvesting resuable assets and demos, sales pitches.
- Communicate complex AI concepts and results to non-technical stakeholders.
- Required Qualifications: Education: Bachelor’s or greater degree in Machine Learning, AI, or equivalent professional experience Experience: Minimum of 1 year of professional experience in AI, application development, machine learning, or a similar role.
- Experience in model deployment, MLOps, model monitoring, and managing data/model drift.
- Experience with predictive AI (e.g., regression, classification, clustering) and generative AI models (e.g., GPT, Claude LLM, Stable Diffusion).
- Technical Skills: Proficiency in programming languages such as Python and SQL.
- Proficiency in URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation.
- Proficiency with cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Databricks, PySpark).Familiarity with AI frameworks such as LangChain and machine learning libraries like TensorFlow, PyTorch, and scikit-learn.Knowledge of deployment tools (e.g., Azure DevOps, Docker, AWS ECS/EKS/Fargate) and CI/CD pipelines (AWS CloudFormation, CodeDeploy).Understanding of data engineering principles, including experience with SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis).
- Additional Skills: Strong problem-solving and troubleshooting skills.
- Familiarity with generative AI techniques, such as retrieval-augmented generation (RAG) patterns.Experience with Graph database technology a plus. (e.g.
- Neo4J, Ontotext)Ability to collaborate effectively across teams.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
Preferred Skills
- AI & Machine Learning Models Development: Develop generative and predictive AI models (including NLP, computer vision, etc.).Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and big data tools (e.g., Databricks, PySpark) to develop AI solutions.
- Familiarity with agentic workflows that utilize AI agents to automate tasks and improve operational efficiency.
- Emerging Technologies: maintain involvement with internal and external training and relevant discussions; stay at the forefront of emerging AI techniques, tools, and trends.
- Collaboration & Communication: Collaborate with cross-functional teams to align AI projects with business requirements and strategic goals.
- Willingness to contribute to and participate in developing and harvesting resuable assets and demos, sales pitches.
- Experience with predictive AI (e.g., regression, classification, clustering) and generative AI models (e.g., GPT, Claude LLM, Stable Diffusion).
- Proficiency with cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Databricks, PySpark).Familiarity with AI frameworks such as LangChain and machine learning libraries like TensorFlow, PyTorch, and scikit-learn.Knowledge of deployment tools (e.g., Azure DevOps, Docker, AWS ECS/EKS/Fargate) and CI/CD pipelines (AWS CloudFormation, CodeDeploy).Understanding of data engineering principles, including experience with SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis).
- Familiarity with generative AI techniques, such as retrieval-augmented generation (RAG) patterns.Experience with Graph database technology a plus. (e.g.
Education
- (Required) – Required Qualifications: Education: Bachelor’s or greater degree in Machine Learning, AI, or equivalent professional experience Experience: Minimum of 1 year of professional experience in AI, application development, machine learning, or a similar role.
We are seeking a passionate and innovative GenAI Engineer/Data Scientist to join our team. This role involves developing GEN AI solutions and predictive AI models, deploying them in production environments, and driving the integration of AI technologies across our business operations. As a key member of our AI team, you will collaborate with diverse teams to design solutions that deliver tangible business value through AI-driven insights. Key Responsibilities: Application Architecture Design, Development, & Integration: Familiarity with API architecture, and components such as external interfacing, traffic control, runtime execution of business logic, data access, authentication, deployment. Key skills to include Understanding of URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation. AI & Machine Learning Models Development: Develop generative and predictive AI models (including NLP, computer vision, etc.).Familiarity with cloud platforms (e.g., Azure, AWS, GCP) and big data tools (e.g., Databricks, PySpark) to develop AI solutions. Familiarity with intelligent autonomous agents for complex tasks and multimodal interactions. Familiarity with agentic workflows that utilize AI agents to automate tasks and improve operational efficiency. Model Deployment & Maintenance: Deploy AI models into production environments, ensuring scalability, performance, and optimization. Monitor and troubleshoot deployed models and pipelines for optimal performance. Design and maintain data pipelines for efficient data collection, processing, and storage (e.g., data lakes, data warehouses). Emerging Technologies: maintain involvement with internal and external training and relevant discussions; stay at the forefront of emerging AI techniques, tools, and trends. Collaboration & Communication: Collaborate with cross-functional teams to align AI projects with business requirements and strategic goals. Willingness to contribute to and participate in developing and harvesting resuable assets and demos, sales pitches. Communicate complex AI concepts and results to non-technical stakeholders. Required Qualifications: Education: Bachelor’s or greater degree in Machine Learning, AI, or equivalent professional experience Experience: Minimum of 1 year of professional experience in AI, application development, machine learning, or a similar role. Experience in model deployment, MLOps, model monitoring, and managing data/model drift. Experience with predictive AI (e.g., regression, classification, clustering) and generative AI models (e.g., GPT, Claude LLM, Stable Diffusion). Technical Skills: Proficiency in programming languages such as Python and SQL. Proficiency in URLs and API Endpoints, HTTP Requests, Authentication Methods, Response Types, JSON/REST, Parameters and Data Filtering, Error Handling, Debugging, Rate Limits, Tokens, Integration, and Documentation. Proficiency with cloud platforms (e.g., AWS, Azure) and big data tools (e.g., Databricks, PySpark).Familiarity with AI frameworks such as LangChain and machine learning libraries like TensorFlow, PyTorch, and scikit-learn.Knowledge of deployment tools (e.g., Azure DevOps, Docker, AWS ECS/EKS/Fargate) and CI/CD pipelines (AWS CloudFormation, CodeDeploy).Understanding of data engineering principles, including experience with SQL and NoSQL databases (e.g., MySQL, MongoDB, Redis). Additional Skills: Strong problem-solving and troubleshooting skills. Familiarity with generative AI techniques, such as retrieval-augmented generation (RAG) patterns.Experience with Graph database technology a plus. (e.g. Neo4J, Ontotext)Ability to collaborate effectively across teams. Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.