Oferta wygasa za:34 dni
Tryb pracyZdalna
Rodzaj umowyKontrakt B2B
Tryb rekrutacjiRekrutacja stacjonarna
Wymiar etatuPełny etat
About the project
The Technical Development Lead owns the technical delivery of the GenAI chatbot. You act as the "Technical Conscience" of the project, bridging the gap between internal business stakeholders and the external software vendor. Your primary goal is to ensure the vendor delivers high-quality, secure, and scalable code that integrates with our internal IT systems and data landscape, while strictly adhering to our AI safety and data privacy standards.
Your responsibilities
- Model Selection: Guide the decision-making process on which LLMs to use based on cost, latency, and performance trade-offs.
- Evaluation Framework: Mandate a rigorous testing framework (using tools like RAGAS or TruLens) to quantitatively measure the chatbot's accuracy, groundedness, and answer relevance before any release.
- Architectural decision: Guides the architecture decisions and aligns with other products in the same area
- API Gateway Guardian: You hold the keys to the internal kingdom. You design the safe API interfaces the vendor uses to access internal customer data, ensuring they never have direct/unsafe access to core databases.
- Data Privacy (PII) Compliance: Enforce strict PII masking protocols. Ensure user queries are sanitized before they are sent to any external LLM provider.
- Identity & Access: oversee the implementation of authentication (e.g., OAuth2, SSO) to ensure the chatbot respects user permission levels (i.e., the bot shouldn't summarize documents the specific user doesn't have access to).
Our requirements
- GenAI Stack: Experience with Python, Langfuse, Vector Database (MongoDB).
- Cloud Architecture: Strong grasp of cloud infrastructure (AWS) to validate the vendor’s deployment strategy.
- Databases: Knowledge of Snowflake and MongoDB
- DevOps: Familiarity with CI/CD pipelines (Gitlab) to ensure the vendor’s deployment process is automated and safe.
- QA: Good understanding of automated testing best practices for frontend, backend and LLM testing
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