Serve as a member of the AI/ML Acceleration team, contributing to the development, evaluation, and deployment of generative AI solutions and AI agent systems.
Work on activities related to LLM integration, prompt engineering, agent development, and GenAI experimentation by collaborating closely with ML engineers, data scientists, stakeholders, and end users.
Developing and experimenting with generative AI applications and AI agents, including prompt engineering, LLM integration, agent workflow design, and evaluation using Python and relevant frameworks (LangChain, LlamaIndex, or similar) with guidance from senior team members.
Contributing to the improvement and optimization of AI agent systems, LLM-powered applications, and generative AI pipelines, including prompt refinement, response quality evaluation, agent reasoning improvements, and experimentation with different models and orchestration approaches.
Analyzing GenAI and agent-related problems and supporting the creation of solutions involving prompt design, agent architecture, retrieval-augmented generation (RAG) systems, evaluation frameworks, tool integration, and deployment strategies under the guidance of senior team members.
Supporting product quality and timeliness by conducting agent testing, validating LLM outputs, assessing response accuracy and safety, documenting experiments and prompt iterations, participating in code reviews, and providing status updates on assigned tasks and learning progress.
Exploring and evaluating emerging AI tools, models, and techniques to identify opportunities for innovation and improvement in existing AI/ML solutions.
Creating documentation, demonstrations, and training materials to communicate GenAI capabilities, agent functionalities, and technical implementations to both technical and non-technical audiences.

Requirements

Basic Python programming knowledge
Introduction to or coursework using any ML libraries (e.g., scikit-learn, TensorFlow, PyTorch)
Familiarity with data structures and basic algorithms
AI/ML Knowledge
Understanding of fundamental ML concepts from coursework (e.g., what supervised/unsupervised learning means)
Interest in AI/ML applications and willingness to learn
Exposure to basic statistical concepts
Has completed a basic ML project in class or independently (Desired)
Familiarity with Jupyter notebooks (Desired)
Basic understanding of what LLMs or AI agents are (Desired)
Curiosity about emerging AI tools and applications (Desired)

Benefits

healthcare
wellness
financial
retirement
family support
continuing education
time off benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonLLM integrationprompt engineeringagent developmentgenerative AI applicationsLangChainLlamaIndexscikit-learnTensorFlowPyTorch

Soft Skills

collaborationcommunicationproblem-solvingcuriositywillingness to learndocumentationevaluationtestingstatus updatestraining