About the Role
We are seeking a Lead Engineer with expertise in Large Language Models (LLMs) to drive our generative AI application development. The role requires the use of advanced AI technologies, such as LLM Serving Frameworks and Retrieval-Augmented Generation (RAG), to innovate
and improve AI applications for various purposes. The ideal candidate should have practical experience in fine-tuning AI models, utilizing the OpenAI API, and employing open-source LLMs to craft cutting-edge applications. Proficiency with LLM development frameworks, particularly Langchain, is essential. The primary goal is to develop and enhance our generative AI capabilities to tackle complex software engineering challenges.
Requirements
Design, implement, and maintain LLM Serving Platforms to support the deployment and scaling of large language models.
Work with Retrieval-Augmented Generation (RAG) to improve the information retrieval capabilities of our models, enhancing their accuracy and efficiency.
Conduct fine-tuning of LLMs using diverse datasets to tailor models for specific applications and ensure optimal performance.
Utilize API from popular development platforms like Jira, Github, Snowflake, Databricks, Datadog to build Gen AI tool for software development.
Utilize the OpenAI API and open-source LLMs in building Gen AI Apps.
Leverage Langchain or similar LLM Application development frameworks for efficient AI model development and deployment.
Utilize API from popular development platforms like Jira, Github, Snowflake, Databricks, Datadog to build Gen AI tool for software development
Who we're looking for
Your expertise in Gen AI and LLM Applications is both profound and practical.
You have a rich background working with cloud data platforms (e.g., Snowflake, Databricks, AWS Redshift, Google BigQuery, Azure), DevOps tools (e.g., Atlassian), cloud SaaS solutions (e.g., Salesforce), and contemporary testing tools/frameworks.
You are well-versed in the application interfaces of cloud data platforms and observability tools (e.g., Datadog, Splunk).
You have hands-on experience in LLM Prompt Engineering, including techniques such as Retrieval Augmented Generation, Self-Consistency, and Prompt Chaining.
You are expert in using APIs from OpenAI, Atlassian, Github, and other prominent development platforms.
About the Company
Purgo AI enhances the delivery of cloud-native and data applications using generative AI for requirements engineering, functional testing, query testing, and data integrity.
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Comprehensive Requirements Generation: Uses fine-tuned large language models (LLMs) to create detailed behavior-driven (BDD) requirements from sparse feature requests. It identifies gaps and generates questionnaires to fill them, rendering requirements in an AI-friendly format for code generation.
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Proactive Functional Testing: Generates functional tests for application interfaces and API queries before code development, ensuring high-quality assurance. Tests are applied to the developed software, generating bug reports for identified issues.
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Automated Post-Production Analysis: Connects log data back to requirements, automating triage, analysis, and response to escalation tickets after software deployment.
Purgo AI works with early customers and design partners on generative AI-driven requirements and test automation for cloud-native data apps, covering use cases like business intelligence, forecasting, and machine learning over data lakes such as Snowflake and Databricks. It integrates with Jira, Github, testing platforms (e.g., Selenium, Pytest), and data warehouse APIs. Customers use Purgo AI for capturing BDD requirements, functional testing, data integrity, and observability.
Leadership: Co-founder and CTO Sang Kim, an engineering leader with experience at VMware and BlackBerry.
Location: Based in Palo Alto, CA, co-created by The Hive, a venture studio specializing in data and AI for enterprises.
​Please email jobs@purgo.ai
Purgo AI is an affirmative action employer and welcomes candidates who will contribute to the diversity of the company.