Website:
clevanoo.com
Job details:
Overview
We are seeking a specialized AI/ML Engineer/ Architect to spearhead the development of a conversational AI interface using Snowflake Cortex. This role focuses on the "Intelligence" layer of our POC—implementing advanced Retrieval-Augmented Generation (RAG) and agentic workflows to transform raw business data into high-fidelity insights for PMs, Sales, and Marketing teams.
Key Responsibilities
• AI Architecture & Strategy: Lead the design of the Cortex Search strategy, selecting optimal embedding models and chunking strategies to maximize retrieval precision.
• Advanced RAG Implementation: Build and optimize RAG workflows using Snowflake’s native AI functions; refine the "Retrieval" stage through hybrid search (semantic + keyword) and re-ranking techniques.
• Agentic Framework Development: Design and implement Cortex Agents using the Planner-Tool-Executor-Refiner framework to handle complex, multi-step GTM queries.
• Prompt Engineering & Model Tuning: Develop and version-control system prompts; utilize Snowflake Cortex functions for text summarization, translation, and sentiment analysis to enrich the user experience.
• LLM Evaluation (LLM-as-a-Judge): Establish an evaluation framework to measure the quality of AI responses (faithfulness, relevancy, and toxicity) and monitor for hallucination.
• Compute & Inference Optimization: Manage and optimize the use of Snowflake’s AI-ready compute, ensuring a balance between model performance (latency) and credit consumption.
Required Skills & Experience
• 8+ years in AI, ML, or Data Engineering, with at least 2 years focused on LLM application development.
• Snowflake Cortex Mastery: Deep hands-on experience with CORTEX.COMPLETE, CORTEX.EMBED_TEXT, and Cortex Search.
• Vector Mechanics: Strong understanding of vector databases, cosine similarity, and high-dimensional indexing within Snowflake.
• RAG Expertise: Proven experience in chunking strategies (recursive, fixed-size) and managing the "context window" for LLMs.
• Python & SQL Proficiency: Ability to write sophisticated Python UDFs and Snowpark code for ML preprocessing.
• Unstructured Data Science: Experience transforming raw, noisy text (Gong transcripts, PDFs) into clean, machine-readable formats for AI consumption.
• Agentic Knowledge: Understanding of how to build "Loop-based" AI systems that can use tools (SQL API, web search, etc.) to solve problems.
We are looking for someone who is experienced in building lasting relationships and is passionate about making meaningful contributions to our team. Don't be mistaken, this is a challenging career path but also highly rewarding. Are you up for the challenge? If so, stop reading and start applying.
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