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I'm Mohammed Muneebuddin, a Senior AI/ML Engineer with 8 years of experience designing, developing, and deploying scalable machine learning, Generative AI, and data-driven solutions across fintech, payments, healthcare, and insurance domains. I have built RAG-based GenAI systems, real-time ML platforms, and intelligent decision-support tools for fraud detection, transaction risk analysis, demand forecasting, and operational optimization using large-scale datasets. I design end-to-end ML pipelines in Python, deploy production-grade APIs with FastAPI and Flask, and implement MLOps practices using MLflow, Terraform, Docker, Kubernetes, and CI/CD to ensure scalability, reproducibility, monitoring, and continuous improvement of AI systems. I also integrate LLMs, prompt engineering, LangChain, and vector databases to create contextual AI applications that support fraud investigations and automated decision-making across enterprise environments.…I'm Mohammed Muneebuddin, a Senior AI/ML Engineer with 8 years of experience designing, developing, and deploying scalable machine learning, Generative AI, and data-driven solutions across fintech, payments, healthcare, and insurance domains. I have built RAG-based GenAI systems, real-time ML platforms, and intelligent decision-support tools for fraud detection, transaction risk analysis, demand forecasting, and operational optimization using large-scale datasets. I design end-to-end ML pipelines in Python, deploy production-grade APIs with FastAPI and Flask, and implement MLOps practices using MLflow, Terraform, Docker, Kubernetes, and CI/CD to ensure scalability, reproducibility, monitoring, and continuous improvement of AI systems. I also integrate LLMs, prompt engineering, LangChain, and vector databases to create contextual AI applications that support fraud investigations and automated decision-making across enterprise environments.

Mohammed Muneebuddin

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I'm Mohammed Muneebuddin, a Senior AI/ML Engineer with 8 years of experience designing, developing, and deploying scalable machine learning, Generative AI, and data-driven solutions across fintech, payments, healthcare, and insurance domains. I have built RAG-based GenAI systems, real-time ML platforms, and intelligent decision-support tools for fraud detection, transaction risk analysis, demand forecasting, and operational optimization using large-scale datasets. I design end-to-end ML pipelines in Python, deploy production-grade APIs with FastAPI and Flask, and implement MLOps practices using MLflow, Terraform, Docker, Kubernetes, and CI/CD to ensure scalability, reproducibility, monitoring, and continuous improvement of AI systems. I also integrate LLMs, prompt engineering, LangChain, and vector databases to create contextual AI applications that support fraud investigations and automated decision-making across enterprise environments.…I'm Mohammed Muneebuddin, a Senior AI/ML Engineer with 8 years of experience designing, developing, and deploying scalable machine learning, Generative AI, and data-driven solutions across fintech, payments, healthcare, and insurance domains. I have built RAG-based GenAI systems, real-time ML platforms, and intelligent decision-support tools for fraud detection, transaction risk analysis, demand forecasting, and operational optimization using large-scale datasets. I design end-to-end ML pipelines in Python, deploy production-grade APIs with FastAPI and Flask, and implement MLOps practices using MLflow, Terraform, Docker, Kubernetes, and CI/CD to ensure scalability, reproducibility, monitoring, and continuous improvement of AI systems. I also integrate LLMs, prompt engineering, LangChain, and vector databases to create contextual AI applications that support fraud investigations and automated decision-making across enterprise environments.

Available to hire

I’m Mohammed Muneebuddin, a Senior AI/ML Engineer with 8 years of experience designing, developing, and deploying scalable machine learning, Generative AI, and data-driven solutions across fintech, payments, healthcare, and insurance domains. I have built RAG-based GenAI systems, real-time ML platforms, and intelligent decision-support tools for fraud detection, transaction risk analysis, demand forecasting, and operational optimization using large-scale datasets.

I design end-to-end ML pipelines in Python, deploy production-grade APIs with FastAPI and Flask, and implement MLOps practices using MLflow, Terraform, Docker, Kubernetes, and CI/CD to ensure scalability, reproducibility, monitoring, and continuous improvement of AI systems. I also integrate LLMs, prompt engineering, LangChain, and vector databases to create contextual AI applications that support fraud investigations and automated decision-making across enterprise environments.

Skills

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Expert

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Work Experience

Senior AI/ML Engineer at Visa

June 1, 2025 - Present

Senior AI/ML Engineer driving RAG-based GenAI solutions and real-time ML systems within Visa’s Advanced Authorization (VAA) platform to support fraud investigation and transaction risk analysis. Built GenAI-powered investigation and decision-support tools with Retrieval Augmented Generation, multi-agent orchestration, and agentic workflows, enabling analysts to retrieve contextual insights from historical fraud cases, policy documentation, transaction patterns, and compliance records while maintaining human-in-the-loop controls. Implemented retrieval pipelines with LangChain, vector databases, and embedding models to support semantic retrieval over unstructured fraud case data and structured transaction metadata. Leveraged AWS Bedrock foundation models and prompt orchestration frameworks to prototype grounded fraud investigation assistants to retrieve and synthesize insights from historical fraud cases, transaction intelligence, and policy documentation. Implemented embedding pipelin

Machine Learning Engineer at Dollar Tree

January 1, 2024 - May 1, 2025

Developed demand forecasting models (XGBoost, LightGBM, Random Forest, Prophet) to improve forecast accuracy across products and stores, enabling better inventory planning and replenishment decisions. Built end-to-end ML pipelines for data preprocessing, feature engineering, training, validation, and performance tuning. Enhanced forecasting during seasonal and promotional periods by incorporating seasonality, promotions, holidays, and regional demand patterns. Engineered store-level, product-level, inventory, pricing, and promotional features from large-scale retail datasets. Deployed forecasting models via Flask/FastAPI services for batch and inventory planning applications. Processed large-scale retail datasets with BigQuery, Spark, and Databricks, enabling automated forecasting and inventory optimization workflows. Used Vertex AI and Gemini-powered services for model experimentation, training orchestration, forecast explanation generation, and deployment management, while leveraging

Senior Data Engineer at Max Healthcare

May 1, 2021 - August 1, 2023

Led the development of a centralized data platform consolidating patient, lab, and operational data from multiple hospital systems. Automated data pipelines using Python and Azure Data Factory to replace manual reporting and reduce data delays. Integrated heterogeneous data sources including HL7/FHIR APIs, relational databases, and third-party healthcare systems for analytics access. Built SQL-based data models and optimized schemas in data warehouses to support real-time reporting. Implemented data validation, cleansing, and transformation frameworks to ensure data quality and governance. Designed near real-time ingestion pipelines using streaming frameworks and batch orchestration tools, reducing reporting latency for critical metrics. Established data governance and lineage tracking for auditability and compliance with healthcare standards. Collaborated with clinical, compliance, and operational teams to enable dashboards in Power BI/Tableau for patient outcomes, resource utilizatio

Python Developer at PolicyBazaar

March 1, 2018 - April 1, 2021

Developed and maintained RESTful APIs using Flask to support policy purchase, renewal, and claims workflows, enabling seamless interaction between frontend applications and insurer systems. Built backend business logic for premium calculations, policy eligibility validation, and real-time quote aggregation from multiple providers. Integrated third-party insurer systems via REST and SOAP APIs to ensure reliable data exchange. Designed and optimized PostgreSQL schemas to support high-volume transactional workloads. Implemented data validation and error-handling frameworks to ensure data accuracy. Automated backend workflows for policy updates, claims tracking, and reporting, reducing manual processing. Containerized applications with Docker and deployed services across staging and production environments for scalable delivery. Created internal tools and scripts for operational reporting and analytics. Collaborated with QA to design unit and integration tests using PyTest, improving relia