Brooksource
Gen AI Developer
Salary
-
Job type
Contractor
Location
Houston, Texas, US
Remote
No
Posted
Yesterday
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Junior Generative AI Developer
Hybrid
ABOUT THE ROLE
Our client is seeking a Fresher / Junior Generative AI Developer who is passionate about AI and Large Language Models (LLMs). In this entry-level role, you will work closely with senior engineers to build, test, and deploy LLM-based applications on AWS, while learning best practices around scalability, cost efficiency, and production readiness. This is an excellent opportunity for recent graduates or early-career professionals eager to grow in the Generative AI and cloud ecosystem. You will gain hands-on experience with state-of-the-art AI technologies, contribute to innovative projects, and develop your skills in a collaborative, cross-functional environment.
WHAT YOU'LL DO
- Assist in developing and maintaining LLM-powered applications using APIs and GenAI frameworks
- Support prompt engineering, experimentation, and evaluation of LLM outputs
- Help build and test Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector search
- Work with AWS services (S3, Lambda, EC2, SageMaker, Bedrock) under guidance from senior developers
- Participate in model training, fine-tuning, and inference workflows
- Monitor application performance, basic cost metrics, and logs
- Write clean, maintainable Python code and unit tests
- Collaborate with cross-functional teams including ML engineers and backend developers
- Learn and follow security, scalability, and cost-optimization best practices
- Document solutions and contribute to team knowledge sharing
WHAT YOU BRING
- Bachelor’s degree in Computer Science, Engineering, AI/ML, or related field (or equivalent experience)
- Basic understanding of Generative AI concepts and Large Language Models (LLMs)
- Proficiency in Python
- Familiarity with AWS fundamentals (S3, EC2, Lambda, SageMaker, or Bedrock)
- Understanding of prompt engineering and API-based LLM usage
- Basic knowledge of machine learning concepts (training, evaluation, inference)
- Exposure to Retrieval-Augmented Generation (RAG) concepts, embeddings, or vector databases (projects or coursework acceptable)
- Familiarity with monitoring/logging tools (e.g., CloudWatch, Datadog) at a basic level
- Familiarity with Git/version control
- Willingness to learn, strong problem-solving skills, and good communication
WHAT'S IN IT FOR YOU
- Opportunity to work with cutting-edge Generative AI and cloud technologies
- Mentorship from experienced engineers and exposure to best practices in AI development
- Collaborative and supportive team environment
- Professional growth and learning opportunities in a rapidly evolving field
- Flexible hybrid work arrangement
Responsibilities
- In this entry-level role, you will work closely with senior engineers to build, test, and deploy LLM-based applications on AWS, while learning best practices around scalability, cost efficiency, and production readiness
- Assist in developing and maintaining LLM-powered applications using APIs and GenAI frameworks
- Support prompt engineering, experimentation, and evaluation of LLM outputs
- Help build and test Retrieval-Augmented Generation (RAG) pipelines using embeddings and vector search
- Work with AWS services (S3, Lambda, EC2, SageMaker, Bedrock) under guidance from senior developers
- Participate in model training, fine-tuning, and inference workflows
- Monitor application performance, basic cost metrics, and logs
- Write clean, maintainable Python code and unit tests
- Collaborate with cross-functional teams including ML engineers and backend developers
- Learn and follow security, scalability, and cost-optimization best practices
- Document solutions and contribute to team knowledge sharing
Qualifications
- Bachelor’s degree in Computer Science, Engineering, AI/ML, or related field (or equivalent experience)
- Basic understanding of Generative AI concepts and Large Language Models (LLMs)
- Proficiency in Python
- Familiarity with AWS fundamentals (S3, EC2, Lambda, SageMaker, or Bedrock)
- Understanding of prompt engineering and API-based LLM usage
- Basic knowledge of machine learning concepts (training, evaluation, inference)
- Exposure to Retrieval-Augmented Generation (RAG) concepts, embeddings, or vector databases (projects or coursework acceptable)
- Familiarity with monitoring/logging tools (e.g., CloudWatch, Datadog) at a basic level
- Familiarity with Git/version control
- Willingness to learn, strong problem-solving skills, and good communication
- Mentorship from experienced engineers and exposure to best practices in AI development
Benefits
- Opportunity to work with cutting-edge Generative AI and cloud technologies
- Collaborative and supportive team environment
- Professional growth and learning opportunities in a rapidly evolving field
- Flexible hybrid work arrangement
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