About KPI Partners
KPI Partners is a 5 times Gartner-recognized data, analytics, and AI consulting company. We are leaders in data engineering on Azure, AWS, Google, Snowflake and Databricks. Founded in 2006, KPI has over 400 consultants and has successfully delivered over 1,000 projects to our clients.
Title: GenAI Engineer (RAG/LLM) - W2 Only
Location: 100% Remote – PST Hours (8 AM – 5 PM PST)
Job Type: Contract – 12 Months
Travel: Should come to the Fremont Office 1 week every 2 months
Key Skills: Python, Huggingface, PyTorch, and Tensorflow.
Nice to Have: Azure, Git, CI/CD, GraphRAG, Fine-tuning, and LLMOps.
About the Role:
As a GenAI Engineer at KPI Partners, you will develop AI-driven enterprise applications, including chatbots on company data. Collaborating with a dedicated team of data scientists and software engineers, you will deliver innovative solutions that drive significant business value and enhance user experiences.
Key Responsibilities:
- Design and develop GenAI-based applications powered by RAG, text-to-SQL, function- calling, and agentic architectures.
- Conduct experiments and analysis to evaluate and optimize the performance and business value impact of GenAI-based applications.
Must-Have Skills & Qualifications:
- 5+ years of experience in software engineering, machine learning, data science, or artificial intelligence.
- Strong proficiency in Python.
- Experience using common NLP and/or ML Python frameworks, such as PyTorch, TensorFlow, Transformers/Hugging Face, and NumPy.
- LLM skills, including fine-tuning, LLMOps, function-calling, and retrieval augmented generation (RAG).
- Experience following software best practices in team settings, including version control (Git), CI/CD, documentation, & unit testing.
- Exposure to Microsoft Azure or a similar cloud computing ecosystem.
- Strong communication skills, and ability to collaborate with cross-functional teams.
- Strong problem-solving skills and the ability to work in a fast-paced, dynamic environment.
Good-to-Have Skills:
- Experience developing GenAI applications leveraging multi-agent frameworks and/or graph-based GenAI approaches (e.g., GraphRAG).
