Vinod Barot

LLM Engineer

https://www.opentalent.in/vinod-barot

Nadiad, Gujarat, India

Key Strengths

Extensive experience (10+ years) in IT, with a strong background in ETL/Data quality testing, AWS, and Snowflake environments.
Direct and recent experience (07/2024-Present) in RAG-based LLM Model Testing, including evaluation of Context Precision, Context Recall, Faithfulness, Factual Correctness, and Response Relevancy using the RAGAS framework.
Proven ability to build AI Agents using CrewAI, demonstrating practical application of LLM orchestration.
Experience in designing and implementing advanced Automated SQL Generator & Executor frameworks utilizing LLMs for Snowflake, indicating expertise in LLM integration with data platforms.
Proficiency in key technologies for data and testing, including Python, Pytest, PySpark, Selenium, API testing (Postman, Python), SQL, and Power BI.
Strong understanding of Data Warehouse, Data Lake, and Data Modelling principles, crucial for data-intensive LLM applications.
Skills explicitly listed on resume such as 'RAG LLM Model Evaluation', 'AI Agents', and 'Prompt Engineer' directly align with the target LLM Engineer role.

Cultural & Operational Fit

Cultural Fit Analysis

The psychometric test score of 0/500 suggests a complete lack of alignment with typical cultural fit indicators, including work attitude, stress handling, and team collaboration. This result is a major red flag and necessitates further investigation to determine actual cultural compatibility.

Soft Skills & Operational Fit

Based on the provided psychometric test score of 0/500, the candidate's soft skills and operational fit are critically low across logical reasoning, work attitude, stress handling, and team collaboration. This score indicates a significant area of concern and requires immediate, thorough re-assessment.

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Assessing your cultural and operational fit

Key Strengths

Extensive experience (10+ years) in IT, with a strong background in ETL/Data quality testing, AWS, and Snowflake environments.
Direct and recent experience (07/2024-Present) in RAG-based LLM Model Testing, including evaluation of Context Precision, Context Recall, Faithfulness, Factual Correctness, and Response Relevancy using the RAGAS framework.
Proven ability to build AI Agents using CrewAI, demonstrating practical application of LLM orchestration.
Experience in designing and implementing advanced Automated SQL Generator & Executor frameworks utilizing LLMs for Snowflake, indicating expertise in LLM integration with data platforms.
Proficiency in key technologies for data and testing, including Python, Pytest, PySpark, Selenium, API testing (Postman, Python), SQL, and Power BI.
Strong understanding of Data Warehouse, Data Lake, and Data Modelling principles, crucial for data-intensive LLM applications.
Skills explicitly listed on resume such as 'RAG LLM Model Evaluation', 'AI Agents', and 'Prompt Engineer' directly align with the target LLM Engineer role.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Vinod Barot

Key Strengths

Cultural & Operational Fit

About

Skills

Key Strengths

Cultural & Operational Fit