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Kartik Pawade

Kartik Pawade

AI Applied Software Engineer

Talent
BengaluruMember since April 4, 2026
PythonJavaSQLGitSpring BootMicroservicesFastAPIHibernateJPARedisRabbitMQCeleryPostgreSQLAgentic RAGLangGraphLangChainMulti-AgentsEmbedding ModelsRAGAS EvaluationGuardrailsVector DatabasesPydantic ValidationDockerKubernetesAWS FargateAWS EKSAWS S3AWS RDSAWS LambdaAWS BedrockCI/CD
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PythonJavaSQLGitSpring Boot

About

Software Engineer with 4 years of experience building scalable backend systems and production AI pipelines. Specialized in microservices, async messaging, and cloud-native deployments across Java and Python stacks, designing and shipping RAG pipelines and multi-agent systems using LangGraph and LangChain — with measurable outcomes in retrieval accuracy and system reliability.

Experience

Software Engineer at Metron Security (2025-09 – 2026-02)

Architected and built production AI pipelines and backend systems with focus on RAG and agentic systems.

  • Architected multi-modal RAG pipeline processing 100GB+ documents (PDF, DOCX, CSV) with 95% accuracy(RAGAS), implementing intelligent chunking, AI-powered summarization for tables/images and vector embeddings.
  • Built agentic AI system using LangGraph with dynamic tool selection, enabling users to query both internal documents and web sources through a single conversational interface.
  • Developed async Ingestion pipeline with S3-presigned url for documents uploads; NextJS frontend featuring chat interface, document upload, ingestion pipeline visualization, configurable RAG strategies (basic/hybrid/multi-query).
  • Engineered scalable backend infrastructure with FastAPI, Celery/Redis task queues, caching, and PostgreSQL with pgVector, RAGAs evals in CI pipeline and streaming responses. With latency 350ms-750ms + Streaming response.
  • Optimized Commander's Service Mode cloud vault sync calls across 15+ commands, cutting response times by ∼40% — improving reliability for app integrations like Slack.

Software Engineer at Cognizant Technology Solutions (2022-08 – 2025-07)

Built scalable backend systems, microservices, and AI pipelines for enterprise clients including Australia's largest telecom.

  • Implemented session-aware RAG Q&A through AWS Lambda and Bedrock Knowledge Base embedded with Amazon Titan Text Embeddings v2, so follow-up questions remain in context.
  • Delivered grounded answers via retrieve-then-generate, with source citations when returned—rather than LLM-only replies.
  • Engineered Order & Cart Microservices for Telstra's 5G network rollout — Australia's largest telecom — across B2B and B2C flows, implementing orchestration-based Saga pattern for order consistency, compensating transactions, and failure recovery.
  • Architected async Excel/PDF report generation and email delivery pipelines using RabbitMQ, ensuring background job processing, decoupled delivery, and zero report loss on service failures.
  • Optimized API performance via schema denormalization and targeted column indexing reducing response time by 30%; cut image size 50% using multi-stage Docker builds, reducing CI/CD pipeline duration.

Education

Bachelor of Engineering in Computer Science – Sant Gadge Baba University, Amravati (2018-08 – 2022-06)

Skills

AI Applied Software Engineer

Reviews

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