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Senior Data & AI Engineer and contract consultant with 6+ years designing enterprise-scale, cloud-native data platforms, production ML systems, and Generative AI (RAG/LLMOps) solutions across Azure, AWS, and GCP. Skilled in real-time ETL/ELT pipelines, distributed systems, data lakes/warehouses, and MLOps CI/CD workflows to reduce cost and accelerate deployment. Core stack includes Python, SQL, PySpark, Snowflake, Databricks, Airflow, Kafka, Docker, Kubernetes, Terraform, dbt, MLflow, and LangChain, with hands-on LLM systems and prompt engineering using Azure OpenAI, AWS Bedrock, and Vertex AI. Available for 6–12 month remote/hybrid contracts with independent, end-to-end delivery.…Senior Data & AI Engineer and contract consultant with 6+ years designing enterprise-scale, cloud-native data platforms, production ML systems, and Generative AI (RAG/LLMOps) solutions across Azure, AWS, and GCP. Skilled in real-time ETL/ELT pipelines, distributed systems, data lakes/warehouses, and MLOps CI/CD workflows to reduce cost and accelerate deployment. Core stack includes Python, SQL, PySpark, Snowflake, Databricks, Airflow, Kafka, Docker, Kubernetes, Terraform, dbt, MLflow, and LangChain, with hands-on LLM systems and prompt engineering using Azure OpenAI, AWS Bedrock, and Vertex AI. Available for 6–12 month remote/hybrid contracts with independent, end-to-end delivery.

Dhruv Patel

AI Engineer, Data Scientist, Data Visualizer, +2

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Senior Data & AI Engineer and contract consultant with 6+ years designing enterprise-scale, cloud-native data platforms, production ML systems, and Generative AI (RAG/LLMOps) solutions across Azure, AWS, and GCP. Skilled in real-time ETL/ELT pipelines, distributed systems, data lakes/warehouses, and MLOps CI/CD workflows to reduce cost and accelerate deployment. Core stack includes Python, SQL, PySpark, Snowflake, Databricks, Airflow, Kafka, Docker, Kubernetes, Terraform, dbt, MLflow, and LangChain, with hands-on LLM systems and prompt engineering using Azure OpenAI, AWS Bedrock, and Vertex AI. Available for 6–12 month remote/hybrid contracts with independent, end-to-end delivery.…Senior Data & AI Engineer and contract consultant with 6+ years designing enterprise-scale, cloud-native data platforms, production ML systems, and Generative AI (RAG/LLMOps) solutions across Azure, AWS, and GCP. Skilled in real-time ETL/ELT pipelines, distributed systems, data lakes/warehouses, and MLOps CI/CD workflows to reduce cost and accelerate deployment. Core stack includes Python, SQL, PySpark, Snowflake, Databricks, Airflow, Kafka, Docker, Kubernetes, Terraform, dbt, MLflow, and LangChain, with hands-on LLM systems and prompt engineering using Azure OpenAI, AWS Bedrock, and Vertex AI. Available for 6–12 month remote/hybrid contracts with independent, end-to-end delivery.

Available to hire

Senior Data & AI Engineer and contract consultant with 6+ years designing enterprise-scale, cloud-native data platforms, production ML systems, and Generative AI (RAG/LLMOps) solutions across Azure, AWS, and GCP. Skilled in real-time ETL/ELT pipelines, distributed systems, data lakes/warehouses, and MLOps CI/CD workflows to reduce cost and accelerate deployment.

Core stack includes Python, SQL, PySpark, Snowflake, Databricks, Airflow, Kafka, Docker, Kubernetes, Terraform, dbt, MLflow, and LangChain, with hands-on LLM systems and prompt engineering using Azure OpenAI, AWS Bedrock, and Vertex AI. Available for 6–12 month remote/hybrid contracts with independent, end-to-end delivery.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Work Experience

Senior Data & AI Engineer at Sanofi — Enterprise Platforms

January 1, 2025 - Present

Architected enterprise-scale generative AI and multi-agent LLM systems (RAG, NL2SQL, knowledge graphs), raising query success to 90%+ and improving grounded-response accuracy (50% to 80%). Built AI automation and document-intelligence pipelines on AWS Bedrock using NLP and microservices to autonomously resolve 65% of L1/L2 tickets and reduce MTTR by 40%. Led LLMOps/MLOps (prompt engineering, model evaluation, human-in-the-loop validation, LLM regression testing) to keep hallucination rates below 10%. Engineered REST and gRPC microservices with Docker/Kubernetes/MLflow and CI/CD to reduce deployments from 10 days to 4 while sustaining 99.9% uptime. Implemented PII-safe AI security, semantic search, and NL2SQL; mentored engineers on scalable AI system and cloud architecture best practices.

Data Scientist / Research Assistant — LLM-Powered Chatbot (RAG) at McMaster University

January 1, 2024 - January 1, 2025

Developed a LangChain-based LLM chatbot to reduce manual user interactions by 30% and improve research workflow efficiency. Built end-to-end ML pipelines integrating AWS EKS, CloudWatch, and MLflow; deployed a Flask REST API for real-time LLM inference orchestrated on Kubernetes for scalable multi-user access. Designed a generative AI lab interface (Hugging Face, OpenAI, LLaMA) for prompt engineering and hyperparameter tuning, systematically improving LLM performance.

Data Engineer — ML Platform Development at EllisDon

January 1, 2023 - January 1, 2024

Built batch and streaming data pipelines using Apache Kafka (real-time ingestion), Airflow (orchestration), and PySpark (processing), provisioned via Terraform to improve pipeline performance by 35%. Developed classification ML pipelines with MLflow tracking churn-propensity and feature-adoption KPI for active-user metrics, contributing to a 4% revenue increase. Deployed production ML pipelines on GCP (BigQuery, Composer, Vertex AI) using statistical inference and multivariate modeling across business verticals.

Assistant Manager — Data & IoT Engineering at Atul Ltd.

January 1, 2019 - January 1, 2022

Led a team building Azure microservices (App Services, Functions, Synapse, Fabric, Key Vault) for real-time server monitoring and alarm management. Reduced critical equipment downtime via ML classification and multivariate forecasting on IoT sensor data, cutting downtime by 16% and adding $12K monthly revenue. Delivered product cycle optimization and energy monitoring (regression and time-series analysis), reducing annual utility consumption by 8%. Managed Kubernetes clusters and Docker containers for deploying and scaling production ML models.