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Hi, I'm Pranathi Guntaka. I'm an AI/ML Engineer with 4+ years of experience building production-grade agentic systems in regulated financial banking. I have a proven track record at Capital One for designing intelligent systems that plan, reason, and act across complex financial workflows. I am proficient in Python, Go, and TypeScript and specialize in auditable RAG pipelines and multi-step reasoning agents that invoke tools and recover gracefully from failures. I enjoy bridging the gap between technical execution and customer outcomes to deliver scalable, enterprise-grade AI solutions.…Hi, I'm Pranathi Guntaka. I'm an AI/ML Engineer with 4+ years of experience building production-grade agentic systems in regulated financial banking. I have a proven track record at Capital One for designing intelligent systems that plan, reason, and act across complex financial workflows. I am proficient in Python, Go, and TypeScript and specialize in auditable RAG pipelines and multi-step reasoning agents that invoke tools and recover gracefully from failures. I enjoy bridging the gap between technical execution and customer outcomes to deliver scalable, enterprise-grade AI solutions.

Pranathi Guntaka

AI Engineer, Data Scientist, Full Stack Developer, +2

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Hi, I'm Pranathi Guntaka. I'm an AI/ML Engineer with 4+ years of experience building production-grade agentic systems in regulated financial banking. I have a proven track record at Capital One for designing intelligent systems that plan, reason, and act across complex financial workflows. I am proficient in Python, Go, and TypeScript and specialize in auditable RAG pipelines and multi-step reasoning agents that invoke tools and recover gracefully from failures. I enjoy bridging the gap between technical execution and customer outcomes to deliver scalable, enterprise-grade AI solutions.…Hi, I'm Pranathi Guntaka. I'm an AI/ML Engineer with 4+ years of experience building production-grade agentic systems in regulated financial banking. I have a proven track record at Capital One for designing intelligent systems that plan, reason, and act across complex financial workflows. I am proficient in Python, Go, and TypeScript and specialize in auditable RAG pipelines and multi-step reasoning agents that invoke tools and recover gracefully from failures. I enjoy bridging the gap between technical execution and customer outcomes to deliver scalable, enterprise-grade AI solutions.

Available to hire

Hi, I’m Pranathi Guntaka. I’m an AI/ML Engineer with 4+ years of experience building production-grade agentic systems in regulated financial banking. I have a proven track record at Capital One for designing intelligent systems that plan, reason, and act across complex financial workflows.

I am proficient in Python, Go, and TypeScript and specialize in auditable RAG pipelines and multi-step reasoning agents that invoke tools and recover gracefully from failures. I enjoy bridging the gap between technical execution and customer outcomes to deliver scalable, enterprise-grade AI solutions.

Skills

Experience Level

Expert

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Language

English

Fluent

Work Experience

AI/ML Engineer at State Street

September 1, 2023 - Present

Architected production-grade AI agentic systems for high-stakes financial workflows, reducing manual processing time by 40% and improving speed and reliability. Designed multi-turn reasoning agents integrating with internal banking tools via gRPC, delivering 30% faster verification and improved failure recovery. Deployed high-precision RAG pipelines over sensitive datasets for 50,000+ users, cutting manual research by 35%. Built LLM-powered features to process over 5TB of unstructured data and implemented LangChain-based orchestration with performance tracking across 5+ global lines, boosting development efficiency by 25%. Achieved 100ms latency targets for AI features while maintaining regulatory compliance.

Jr. AI/ML Engineer at TopHat Software Technologies

July 1, 2020 - August 1, 2022

Engineered high-performance backend systems using Python and Go with Apache Kafka to support scalable ML pipelines (1M+ daily requests). Optimized large-scale SQL queries and distributed schemas to boost throughput by 25%. Automated data transformation and feature engineering pipelines, reducing manual effort by 40% and reclaiming 60+ hours of monthly bandwidth. Contributed to production-grade ML engineering standards, implemented AI safety guardrails for production LLMs, and supported 99.9% system uptime with robust monitoring. Applied RAG pipelines on AWS and Kubernetes to improve data access efficiency by 30% while ensuring auditability.