Piyush K.

Software Engineer

Bengaluru, Karnataka, India3 yrs 7 mos experience
Most Likely To SwitchAI ML Practitioner

Key Highlights

  • Expert in optimizing APIs for real-time performance.
  • Proficient in Generative AI and LLM technologies.
  • Strong background in microservices architecture.
Stackforce AI infers this person is a Backend-focused Software Engineer specializing in SaaS solutions with expertise in Generative AI.

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Skills

Core Skills

MicroservicesPythonLarge Language Models (llm)

Other Skills

API OptimizationAWS BedrockAWS OpensearchAirflowBigQueryCC (Programming Language)C++CachingCascading Style Sheets (CSS)CeleryConcurrent ProgrammingData StructuresDockerDynamic Programming

About

At Tredence Inc., the team and I are at the forefront of innovation in Generative AI, focusing on backend systems integration, design and development that drive efficiency and user satisfaction.

Experience

Tredence inc.

2 roles

Software Developer II

Promoted

Jan 2024Present · 2 yrs 2 mos · Bengaluru, Karnataka, India · Hybrid

  • Integrated server-sent events (SSE) in scalable chat APIs to handle high traffic, ensuring low latency, and
  • increasing engagement by 10%
  • Optimized multiple Chat APIs by integrating Python generators with SSE, enabling real-time streaming responses that significantly reduced latency and boosted user retention
  • Integrated Google Pub/Sub to stream agentic AI thoughts to the frontend in real-time, enhancing user experience and enabling seamless visualization of multi-agent reasoning processes.
Low-Level DesignMicroservicesPythonRedisGoogle Pub/SubReal-time Streaming+1

Software Developer I

Jul 2022Dec 2023 · 1 yr 5 mos · Bengaluru, Karnataka, India · Hybrid

  • Spearheaded the optimization of table summarization system using LLMs,
  • leveraging asynchronous operations to achieve a 70% reduction in response
  • time, significantly improving user experience.
  • Developed recommendation APIs to get relevant questions based on
  • user-selected tables utilizing concurrent operations, caching, and other
  • optimizations leading to improved API performance by 80%, ensuring an
  • efficient user interaction.
  • Implemented a micro-services architecture to enhance the performance of
  • a recommendation API by offloading processing tasks, including Redis task
  • scheduling and caching from main API resulting in a significant reduction of
  • over 65% in response time
  • Increased vector search precision by 58% based on user-selected criteria
  • within the vector store, optimizing retrieval for RAG search and therefore
  • improving the relevance of search results.
  • Implemented concurrent processing for expensive database operations in
  • python to solve scalability challenges.
  • Devised algorithms to track pipeline states, including success and failure
  • due to various factors, and provided real-time status updates to BigQuery
  • housekeeping tables minimizing downtime by 19%.
  • Conceptualized corrupt file routing algorithms, enhancing pipeline
  • robustness by segregating inconsistent/junk files into a separate bucket
  • leading to optimized resource utilization and reduced processing
  • overheads by 14%.
Retrieval-augmented generationLarge Language Models (LLM)Concurrent ProgrammingCachingMicroservicesRedis+2

Education

National Institute of Technology Mizoram

B.Tech — Electronics and Communications Engineering

Jan 2018Jan 2022

Sri Chaitanya Vidyaniketan, Visakhapatnam

Intermediate

Kendriya Vidyalaya

10th Standard

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