Junior AI Engineer: GenAI & LLMs, Python Apps

Capgemini Engineering; San Francisco (CA); On-site; USD 48,000 - 97,000; Full time

Benefits offered by this job

PTO 12–25 days

Medical, dental, vision insurance

401(k) retirement plan

Life and disability insurance

Employee assistance programs

Job summary

Capgemini Engineering is seeking a Junior AI Engineer in the San Francisco Bay Area to support AI/ML and Generative AI deployment. You will write Python-based services, work with LLMs, embeddings, and vector databases, and contribute to rapid PoCs under senior guidance.

Your role includes data prep, feature engineering, model evaluation, and integrating AI into enterprise workflows, with code reviews and deployment tasks expected.

Qualifications

• Bachelor’s degree in Computer Science, AI/ML, Data Science, Engineering, or a related discipline.

Responsibilities

• Develop and test AI/ML and Generative AI solutions under guidance from senior engineers.
• Build Python-based applications, APIs, and AI integration components.
• Work with LLMs, prompt engineering, RAG, embeddings, and vector databases.
• Assist with data preparation, feature engineering, model evaluation, and testing.
• Integrate AI models/APIs into enterprise applications and workflows.
• Participate in code reviews, debugging, documentation, and deployment activities.
• Explore emerging AI technologies and contribute to rapid prototypes/PoCs.

Skills

Python

SQL

ML Fundamentals

NLP

Generative AI

LLMs

LangChain/LlamaIndex

PyTorch/TensorFlow

REST APIs

Git & Cloud

Education

Bachelor’s degree in Computer Science, AI/ML, Data Science, Engineering

Tools

LangChain

LlamaIndex

PyTorch

TensorFlow

REST APIs

Git

Cloud platforms

Job description

Capgemini Engineering is seeking a Junior AI Engineer in the San Francisco Bay Area to support AI/ML and Generative AI deployment. You will write Python-based services, work with LLMs, embeddings, and vector databases, and contribute to rapid PoCs under senior guidance.

Your role includes data prep, feature engineering, model evaluation, and integrating AI into enterprise workflows, with code reviews and deployment tasks expected.