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Hi, I’m Shankari Venkatesh, an AI Engineer with hands-on experience building and deploying end-to-end ML systems—from RAG pipelines and LLM fine-tuning to production, monitoring, and cloud infrastructure. I’ve contributed to Google’s Gemini evaluation programme and shipped AI features serving live users, spanning model development, experiment tracking, and responsible AI practices. I’m passionate about building scalable, measurable systems that solve real-world challenges. I thrive in cross-functional teams, enjoy turning complex data problems into robust production solutions, and continually explore how ML can deliver tangible impact while upholding safety and trustworthiness.…Hi, I’m Shankari Venkatesh, an AI Engineer with hands-on experience building and deploying end-to-end ML systems—from RAG pipelines and LLM fine-tuning to production, monitoring, and cloud infrastructure. I’ve contributed to Google’s Gemini evaluation programme and shipped AI features serving live users, spanning model development, experiment tracking, and responsible AI practices. I’m passionate about building scalable, measurable systems that solve real-world challenges. I thrive in cross-functional teams, enjoy turning complex data problems into robust production solutions, and continually explore how ML can deliver tangible impact while upholding safety and trustworthiness.

Shankari Venkatesh

AI Engineer, Data Scientist, Data Analyst, +2

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Hi, I’m Shankari Venkatesh, an AI Engineer with hands-on experience building and deploying end-to-end ML systems—from RAG pipelines and LLM fine-tuning to production, monitoring, and cloud infrastructure. I’ve contributed to Google’s Gemini evaluation programme and shipped AI features serving live users, spanning model development, experiment tracking, and responsible AI practices. I’m passionate about building scalable, measurable systems that solve real-world challenges. I thrive in cross-functional teams, enjoy turning complex data problems into robust production solutions, and continually explore how ML can deliver tangible impact while upholding safety and trustworthiness.…Hi, I’m Shankari Venkatesh, an AI Engineer with hands-on experience building and deploying end-to-end ML systems—from RAG pipelines and LLM fine-tuning to production, monitoring, and cloud infrastructure. I’ve contributed to Google’s Gemini evaluation programme and shipped AI features serving live users, spanning model development, experiment tracking, and responsible AI practices. I’m passionate about building scalable, measurable systems that solve real-world challenges. I thrive in cross-functional teams, enjoy turning complex data problems into robust production solutions, and continually explore how ML can deliver tangible impact while upholding safety and trustworthiness.

Available to hire

Hi, I’m Shankari Venkatesh, an AI Engineer with hands-on experience building and deploying end-to-end ML systems—from RAG pipelines and LLM fine-tuning to production, monitoring, and cloud infrastructure. I’ve contributed to Google’s Gemini evaluation programme and shipped AI features serving live users, spanning model development, experiment tracking, and responsible AI practices. I’m passionate about building scalable, measurable systems that solve real-world challenges.

I thrive in cross-functional teams, enjoy turning complex data problems into robust production solutions, and continually explore how ML can deliver tangible impact while upholding safety and trustworthiness.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

AI/LLM Evaluation Engineer at Uber AI Solutions (contracted to Google)

October 1, 2025 - December 1, 2025

Contributed to the evaluation and quality assurance of Google’s Gemini platform within a cross-functional team focused on large-scale LLM reliability and safety. Strengthened Gemini’s coding benchmark programme by authoring original solutions and delivering peer-level code reviews across multiple languages, improving benchmark coverage by 25% and output reliability by 18%. Identified edge cases, hallucinations, and safety risks; implemented guardrail and validation frameworks that reduced unsafe response rates in targeted eval categories. Built Python-based evaluation pipelines and automated reporting tooling, cutting manual testing overhead by 30% and improving consistency across review cycles. Conducted systematic red-teaming and adversarial prompt testing to surface failure modes, feeding findings into model improvement cycles, reducing critical safety failures by 40%. Developed advanced prompt templates for multi-task LLM applications and collaborated on security compliance rev

AI/ML Engineer at Suportr League

October 1, 2023 - September 1, 2025

Owned the end-to-end ML lifecycle across a live gaming platform, from problem scoping and data pipeline design through model deployment and production monitoring, achieving a 30% improvement in overall system reliability. Architected and shipped RAG pipelines for dynamic content delivery (dense retrieval with FAISS/Chroma, cross-encoder re-ranking, RAGAS-based evaluation) with a 28% lift in response relevance. Built a real-time web scraping news bot integrated with Hugging Face transformers for sentiment analysis, and designed personalised recommendations using XGBoost/LightGBM with deep ranking layers, boosting user retention and CTR. Engineered AI-driven fraud detection, containerised ML workflows with Docker, and deployed via FastAPI on AWS with CI/CD; tracked experiments with MLflow and Weights & Biases.