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Hi, I’m an AI/ML engineer who loves turning complex data into practical AI solutions. I’ve spent 3+ years building production AI systems and large-scale ML pipelines for enterprise and financial platforms, with a focus on Generative AI, LLMs, and real-time inference. I enjoy delivering reliable, scalable AI products that drive measurable business impact.\n\nFrom end-to-end ML pipelines on cloud platforms to retrieval-augmented generation and knowledge copilots, I thrive on solving tough modeling and deployment challenges and collaborating across teams to push the boundaries of what AI can do for real-world applications.…Hi, I’m an AI/ML engineer who loves turning complex data into practical AI solutions. I’ve spent 3+ years building production AI systems and large-scale ML pipelines for enterprise and financial platforms, with a focus on Generative AI, LLMs, and real-time inference. I enjoy delivering reliable, scalable AI products that drive measurable business impact.\n\nFrom end-to-end ML pipelines on cloud platforms to retrieval-augmented generation and knowledge copilots, I thrive on solving tough modeling and deployment challenges and collaborating across teams to push the boundaries of what AI can do for real-world applications.

Dishen Jatin Kumar Patel

AI Engineer, Data Scientist, AI Developer, +2

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Hi, I’m an AI/ML engineer who loves turning complex data into practical AI solutions. I’ve spent 3+ years building production AI systems and large-scale ML pipelines for enterprise and financial platforms, with a focus on Generative AI, LLMs, and real-time inference. I enjoy delivering reliable, scalable AI products that drive measurable business impact.\n\nFrom end-to-end ML pipelines on cloud platforms to retrieval-augmented generation and knowledge copilots, I thrive on solving tough modeling and deployment challenges and collaborating across teams to push the boundaries of what AI can do for real-world applications.…Hi, I’m an AI/ML engineer who loves turning complex data into practical AI solutions. I’ve spent 3+ years building production AI systems and large-scale ML pipelines for enterprise and financial platforms, with a focus on Generative AI, LLMs, and real-time inference. I enjoy delivering reliable, scalable AI products that drive measurable business impact.\n\nFrom end-to-end ML pipelines on cloud platforms to retrieval-augmented generation and knowledge copilots, I thrive on solving tough modeling and deployment challenges and collaborating across teams to push the boundaries of what AI can do for real-world applications.

Available to hire

Hi, I’m an AI/ML engineer who loves turning complex data into practical AI solutions. I’ve spent 3+ years building production AI systems and large-scale ML pipelines for enterprise and financial platforms, with a focus on Generative AI, LLMs, and real-time inference. I enjoy delivering reliable, scalable AI products that drive measurable business impact.\n\nFrom end-to-end ML pipelines on cloud platforms to retrieval-augmented generation and knowledge copilots, I thrive on solving tough modeling and deployment challenges and collaborating across teams to push the boundaries of what AI can do for real-world applications.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at JPMorgan Chase & Co.

October 1, 2025 - Present

Designed and deployed a banking recommender system using PySpark and PyTorch, processing 20M+ customer events daily and generating $1.1M cross-sell revenue within the first four months through personalized product recommendations. Developed customer churn prediction models using XGBoost and LightGBM, orchestrated through Airflow on AWS; improved high-risk customer identification and retention campaign conversions by 12–16%. Built a low-latency financial risk scoring service using PyTorch embeddings and FastAPI, deployed via Docker and Kubernetes on AWS EKS, reducing inference latency by 30% while improving recommendation relevance. Implemented an LLM-powered banking knowledge copilot using RAG pipelines, integrating LangChain orchestration with FAISS vector search to retrieve banking knowledge and improve automated support responses accuracy. Developed MLOps pipelines using MLflow and Airflow, enabling automated model training and deployment on AWS SageMaker, reducing production cycl

Machine Learning Engineer at Accenture

January 1, 2022 - August 31, 2024

Built an enterprise AI knowledge assistant using transformer-based LLMs and retrieval-augmented generation, enabling semantic search across 5M+ internal documents and increasing enterprise information retrieval accuracy by 30%. Designed end-to-end ML data pipelines using Spark, Azure ML, and ETL workflows to support scalable ML and LLM applications. Implemented hybrid document retrieval pipelines combining TF-IDF with embedding-based vector search, improving document relevance scoring and reducing information retrieval errors by 25%. Implemented automated MLOps workflows using MLflow, Airflow, and cloud monitoring to enable model drift detection, experiment tracking, and automated retraining, reducing production performance degradation by 35%. Built classifiers and clustering models for enterprise query routing using Logistic Regression, Random Forest, and K-Means to achieve 92% classification accuracy for internal support queries. Deployed ML/LLM inference services on AWS SageMaker, A