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AI Software Engineer with 4+ years of experience building scalable backend systems and designing AI-powered applications. I integrate Large Language Models (LLMs), develop ML pipelines, and build AI-augmented microservices using Python/Java and cloud-native AWS infrastructure. I’ve delivered measurable production impact, including 32% latency reduction and 28% throughput gains, while building secure, event-driven services and RAG solutions for enterprise automation and knowledge assistants.…AI Software Engineer with 4+ years of experience building scalable backend systems and designing AI-powered applications. I integrate Large Language Models (LLMs), develop ML pipelines, and build AI-augmented microservices using Python/Java and cloud-native AWS infrastructure. I’ve delivered measurable production impact, including 32% latency reduction and 28% throughput gains, while building secure, event-driven services and RAG solutions for enterprise automation and knowledge assistants.

Abhignan Reddy Yangangari

AI Engineer, Software Engineer, Back-End Developer, +2

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AI Software Engineer with 4+ years of experience building scalable backend systems and designing AI-powered applications. I integrate Large Language Models (LLMs), develop ML pipelines, and build AI-augmented microservices using Python/Java and cloud-native AWS infrastructure. I’ve delivered measurable production impact, including 32% latency reduction and 28% throughput gains, while building secure, event-driven services and RAG solutions for enterprise automation and knowledge assistants.…AI Software Engineer with 4+ years of experience building scalable backend systems and designing AI-powered applications. I integrate Large Language Models (LLMs), develop ML pipelines, and build AI-augmented microservices using Python/Java and cloud-native AWS infrastructure. I’ve delivered measurable production impact, including 32% latency reduction and 28% throughput gains, while building secure, event-driven services and RAG solutions for enterprise automation and knowledge assistants.

Available to hire

AI Software Engineer with 4+ years of experience building scalable backend systems and designing AI-powered applications. I integrate Large Language Models (LLMs), develop ML pipelines, and build AI-augmented microservices using Python/Java and cloud-native AWS infrastructure.

I’ve delivered measurable production impact, including 32% latency reduction and 28% throughput gains, while building secure, event-driven services and RAG solutions for enterprise automation and knowledge assistants.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Beginner

Beginner

Beginner

Work Experience

AI Software Engineer at ServiceNow

November 1, 2024 - Present

Designed and integrated LLM-powered features into production microservices using OpenAI and LangChain, enabling intelligent automation workflows that reduced manual processing time by 40% across enterprise workflows. Built RAG pipelines leveraging Pinecone vector database and Spring Boot services, improving knowledge retrieval accuracy by 35% for internal AI assistant applications. Engineered high-performance AI-augmented microservices with Spring Boot and REST APIs, reducing average API latency by 32% while supporting 2M+ monthly transactions across distributed systems. Architected scalable event-driven backend services with asynchronous processing, improving throughput by 28% under peak load to enable reliable real-time AI inference at scale. Optimized PostgreSQL queries and indexing strategies, cutting query execution time by 41% for low-latency AI feature pipelines. Implemented secure OAuth2/JWT authentication across AI-powered APIs, reducing unauthorized access incidents by 60% an

Software Engineer at Infinite Infolab

January 1, 2020 - July 1, 2023

Developed AI-assisted user interfaces with React.js and Redux, integrating NLP-powered search and recommendation features that improved frontend responsiveness by 35% and user engagement metrics. Built RESTful APIs with Node.js and Spring Boot to serve ML model predictions, bridging data science pipelines with production applications and increasing data processing efficiency by 30%. Refactored legacy monoliths into modular, microservices-based architecture, reducing code complexity by 40% and creating a foundation suitable for AI feature integration. Integrated MySQL/PostgreSQL with optimized schema design for ML feature storage, improving data consistency and reducing query failures by 25%. Deployed containerized applications with Docker for consistent dev/staging/production environments and reducing setup time by 50%. Strengthened quality with unit and integration testing (JUnit, PyTest), increasing defect detection rate by 38% before production releases of AI-enabled features.