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Hi, I’m Soma Kiran Kumar Nellipudi, a Generative AI and Machine Learning Engineer based in Atlanta with 8+ years of experience building large-scale, secure FinTech and enterprise platforms. I now specialize in LLMs, GenAI systems, and ML-driven automation, focusing on Production-ready AI solutions, intelligent assistants, fraud detection, and data-driven decision systems using cloud-native and MLOps practices. I collaborate closely with product, security, and business stakeholders to translate complex financial requirements into scalable AI-driven solutions. I mentor engineers, help drive AI adoption and governance, and contribute to responsible AI usage in FinTech while designing robust data pipelines, event-driven architectures, and reliable AI deployments across AWS, GCP, and Azure.…Hi, I’m Soma Kiran Kumar Nellipudi, a Generative AI and Machine Learning Engineer based in Atlanta with 8+ years of experience building large-scale, secure FinTech and enterprise platforms. I now specialize in LLMs, GenAI systems, and ML-driven automation, focusing on Production-ready AI solutions, intelligent assistants, fraud detection, and data-driven decision systems using cloud-native and MLOps practices. I collaborate closely with product, security, and business stakeholders to translate complex financial requirements into scalable AI-driven solutions. I mentor engineers, help drive AI adoption and governance, and contribute to responsible AI usage in FinTech while designing robust data pipelines, event-driven architectures, and reliable AI deployments across AWS, GCP, and Azure.

Soma Kiran Kumar Nellipudi

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Hi, I’m Soma Kiran Kumar Nellipudi, a Generative AI and Machine Learning Engineer based in Atlanta with 8+ years of experience building large-scale, secure FinTech and enterprise platforms. I now specialize in LLMs, GenAI systems, and ML-driven automation, focusing on Production-ready AI solutions, intelligent assistants, fraud detection, and data-driven decision systems using cloud-native and MLOps practices. I collaborate closely with product, security, and business stakeholders to translate complex financial requirements into scalable AI-driven solutions. I mentor engineers, help drive AI adoption and governance, and contribute to responsible AI usage in FinTech while designing robust data pipelines, event-driven architectures, and reliable AI deployments across AWS, GCP, and Azure.…Hi, I’m Soma Kiran Kumar Nellipudi, a Generative AI and Machine Learning Engineer based in Atlanta with 8+ years of experience building large-scale, secure FinTech and enterprise platforms. I now specialize in LLMs, GenAI systems, and ML-driven automation, focusing on Production-ready AI solutions, intelligent assistants, fraud detection, and data-driven decision systems using cloud-native and MLOps practices. I collaborate closely with product, security, and business stakeholders to translate complex financial requirements into scalable AI-driven solutions. I mentor engineers, help drive AI adoption and governance, and contribute to responsible AI usage in FinTech while designing robust data pipelines, event-driven architectures, and reliable AI deployments across AWS, GCP, and Azure.

Available to hire

Hi, I’m Soma Kiran Kumar Nellipudi, a Generative AI and Machine Learning Engineer based in Atlanta with 8+ years of experience building large-scale, secure FinTech and enterprise platforms. I now specialize in LLMs, GenAI systems, and ML-driven automation, focusing on Production-ready AI solutions, intelligent assistants, fraud detection, and data-driven decision systems using cloud-native and MLOps practices.

I collaborate closely with product, security, and business stakeholders to translate complex financial requirements into scalable AI-driven solutions. I mentor engineers, help drive AI adoption and governance, and contribute to responsible AI usage in FinTech while designing robust data pipelines, event-driven architectures, and reliable AI deployments across AWS, GCP, and Azure.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Senior Generative AI / Machine Learning Engineer at InComm Payments, USA

December 1, 2022 - Present

Designed and implemented enterprise-grade Generative AI solutions using LLMs to support intelligent transaction analysis, internal knowledge assistants, and operational automation. Built Retrieval-Augmented Generation pipelines integrating structured financial data, documents, and APIs to enable accurate, secure, and auditable AI responses. Applied prompt engineering, embeddings, and semantic search techniques to improve response quality, reduce hallucinations, and enhance user trust. Produced ML-driven fraud and anomaly detection frameworks leveraging high-volume transaction data. Architected scalable AI-ready data pipelines using event-driven systems (Kafka) to support real-time inference and analytics. Productionized GenAI and ML models with Docker and Kubernetes, ensuring reliability, monitoring, and cost efficiency. Partnered with security and compliance teams to ensure HITRUST and ISO-compliant AI systems, including data privacy, access controls, and auditability. Mentored engine

Principal Technical Services Engineer at Manhattan Associates, USA

March 1, 2022 - December 1, 2022

Designed and delivered scalable order fulfillment and warehouse management systems producing high-volume, high-quality operational data suitable for advanced analytics and AI/ML consumption. Built and optimized data pipelines to ingest, cleanse, and transform real-time and batch supply-chain data, enabling demand forecasting, inventory optimization, and operational intelligence use cases. Collaborated with product, data, and engineering teams to support ML-driven forecasting and optimization models, ensuring data accuracy, consistency, and timeliness. Implemented event-driven architectures to capture real-time warehouse and logistics events for predictive and prescriptive analytics. Improved system observability and data reliability, enabling downstream AI-powered decision support systems. Acted as a technical lead and advisor, guiding teams on AI-ready architecture, scalability, and performance best practices for enterprise retail and logistics platforms.

Senior AI / Machine Learning Engineer at InComm Payments, USA

February 1, 2017 - March 1, 2022

Transformed legacy monolithic systems into AI/ML-ready microservices, enabling reliable data ingestion for model training, feature engineering, and analytics. Designed and built scalable data-centric APIs and cloud-native services that powered ML pipelines, improving system response time by 20%. Implemented event-driven architectures (Kafka) to support real-time data streaming, online feature generation, and near real-time ML inference. Collaborated with data science teams to ensure high-quality, labeled, and compliant datasets for fraud detection and anomaly detection models. Established foundational MLOps by integrating CI/CD, monitoring, and versioning to support continuous model experimentation and deployment. Enabled AI-driven decision systems by ensuring secure, compliant, and scalable data flows across financial platforms.

Java Developer at Delta Air Lines, USA

April 1, 2016 - February 1, 2017

Led migration of legacy mainframe components to Java-based microservices, improving system scalability and transforming operational data into structured, analytics-ready formats. Designed and implemented RESTful APIs using Java and Spring to expose booking, hotel, and destination data for reporting, analytics, and early ML experimentation. Built data ingestion and transformation layers to standardize disparate data sources, enabling reliable downstream analytics and feature engineering. Automated hotel and destination onboarding workflows, reducing manual effort and improving data quality. Implemented validation, logging, and monitoring to ensure data accuracy, consistency, and traceability across services. Collaborated with product, QA, and operations teams to define data schemas and integration patterns that supported predictive analytics and optimization use cases.