I’m an AI/ML engineer with 5+ years of experience building production-grade machine learning and GenAI solutions across FinTech, SaaS, and enterprise AI platforms. My work spans credit-risk and propensity modeling, NLP and semantic retrieval, and more recently agentic systems and Agentic RAG workflows—always focused on reliable outcomes from experimentation through deployment.

Prudhvi Reddy Kottam

I’m an AI/ML engineer with 5+ years of experience building production-grade machine learning and GenAI solutions across FinTech, SaaS, and enterprise AI platforms. My work spans credit-risk and propensity modeling, NLP and semantic retrieval, and more recently agentic systems and Agentic RAG workflows—always focused on reliable outcomes from experimentation through deployment.

Available to hire

I’m an AI/ML engineer with 5+ years of experience building production-grade machine learning and GenAI solutions across FinTech, SaaS, and enterprise AI platforms. My work spans credit-risk and propensity modeling, NLP and semantic retrieval, and more recently agentic systems and Agentic RAG workflows—always focused on reliable outcomes from experimentation through deployment.

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Experience Level

Expert
Expert
Expert
Expert
Expert
Intermediate
Intermediate

Work Experience

AI/ML Engineer at Snowflake
May 1, 2025 - Present
Orchestrated enterprise agentic AI workflows using Python, Snowflake Cortex Agents, MCP, FastAPI, and governed access via Snowflake SQL, handling 250+ daily tool-calling requests with human approval and policy enforcement. Architected agentic RAG combining Cortex Search, BM25, dense embeddings, hybrid retrieval, and reranking to improve retrieval relevance by 10% for governed structured and unstructured knowledge applications. Operationalized AI evaluation and observability using Cortex Agent Evaluations, Cortex AI Observability, MLflow, and LLM-as-a-judge, shortening regression-analysis cycles by 15% through golden datasets, tracing, and quality gates. Secured production agents using Horizon Catalog, RBAC, Cortex AI Guardrails, Pydantic, and audit logging with prompt-injection protection and human approval. Deployed production AI/ML services using Snowflake, Docker, Kubernetes, OpenTelemetry, Prometheus, and Grafana, coordinating with platform and security teams to sustain 99.5% avail
Machine Learning Engineer at Freshworks
February 1, 2022 - December 31, 2023
Built ticket classification, routing, and SLA-risk models with Python, SQL, scikit-learn, XGBoost, and BERT, reducing manual triage effort by 8% across 300+ daily tickets. Improved knowledge recommendation pipelines using Sentence-BERT, FAISS, Elasticsearch, and embeddings, increasing article relevance by 9% and supporting workflows for 75+ support agents. Delivered 2023 generative AI workflows using GPT-3.5-turbo, GPT-4, and Azure OpenAI Service with prompt engineering and RAG, reducing agent handling effort by 11% and contributing $18K+ in annualized efficiency savings. Productionized ML services with FastAPI, REST APIs, Docker, Git, and MLflow to support 400+ daily workflows while maintaining 92.4% availability via deployment discipline, model versioning, drift monitoring, retraining, and release validation. Collaborated with product, customer support, data engineering, and backend teams to translate requirements into ML objectives, API contracts, evaluation criteria, and feedback l
Machine Learning Engineer at Navi
May 1, 2020 - January 31, 2022
Developed digital lending credit-risk models using Python, SQL, scikit-learn, XGBoost, and LightGBM, improving precision by 7% across 600+ monthly applications through feature engineering, validation, and underwriting-focused optimization. Engineered risk features spanning debt-to-income, credit utilization, repayment history, delinquencies, and employment stability, applying SHAP, class-imbalance handling, and threshold optimization to improve explainability and underwriting outcomes. Built customer-propensity and loan-conversion models with SQL, scikit-learn, and XGBoost to improve campaign conversion by 8% and enable scoring across 4 customer-growth workflows using segmentation, EDA, and A/B testing. Implemented health-insurance claims-risk and anomaly-detection pipelines using XGBoost and Isolation Forest, reducing manual claim-screening volume by 6% across 400+ monthly claims through risk scoring and human review validation. Integrated models via FastAPI, REST APIs, Docker, and mo

Education

Master of Science in Data Science at University of Alabama, Birmingham
January 1, 2024 - December 31, 2025
Master of Science in Data Science at University of Alabama, Birmingham
January 1, 2024 - December 31, 2025

Qualifications

AWS Certified AI Practitioner (AIF-C01)
January 11, 2030 - September 2, 2026
Microsoft Certified: Azure AI Fundamentals (AI-900)
January 11, 2030 - September 2, 2026
DeepLearning.AI Machine Learning Specialization
January 11, 2030 - September 2, 2026
GIAC Python Coder (GPYC)
January 11, 2030 - September 2, 2026

Industry Experience

Financial Services, Software & Internet