Backend & Agentic AI Engineer

K M BINDU SHREE

Software Engineer I — Backend & AIML Engineer

**************@*****.*** +91-948******* Bangalore, India linkedin.com/in/k-m-bindu-shree-030b28252 PROFESSIONAL SUMMARY

Backend and AIML Engineer with 2+ years of experience building scalable, high-availability microservices and production-grade Agentic AI systems. Skilled in Python, Django, FastAPI, and distributed system design. Built full-stack AI platforms using LangChain, LangGraph, and MCP-based multi-agent orchestration with RAG pipelines on AWS Bedrock. Experienced in REST API design, system optimization, LLM evaluation, and high-performance backend architectures, achieving up to 90% latency reduction. Familiar with supervised and unsupervised learning techniques, model evaluation metrics, and data processing using NumPy, Pandas, and Scikit- learn.

SKILLS

Languages & Frameworks: Python, Django, Django REST Framework, FastAPI, Next.js Agentic AI & LLM Systems: LangChain, LangGraph, MCP, RAG, AWS Bedrock (Claude) LLM Evaluation & Testing: DeepEval, LLM-as-Judge, Hallucination Detection, Faithfulness & Answer Relevancy, Context Precision/Recall, Regression Testing for LLMs

Data & Infrastructure: MySQL, Redis, Elasticsearch, Docker, Celery, OpenTelemetry, Docling, Git Speech & Audio Processing: Speech-to-Text / ASR, Real-time Transcription, AssemblyAI ML Fundamentals: Supervised & Unsupervised Learning, Regression & Classification Algorithms, NumPy, Pandas, Scikit-learn WORK EXPERIENCE

Software Engineer I Oct 2024 – Present

1Wrk Bangalore

● Agent Orchestrator: Architected and delivered a full-stack, high-availability Agent Orchestrator (Python, LangChain, Redis, Next.js) in a microservices architecture, independently building its Next.js frontend without frontend-team dependencies.

● MCP Architecture & Observability: Designed MCP-based distributed system architecture for modular Agentic AI workflows, including an orchestration layer with OpenTelemetry-based distributed tracing that improved debugging efficiency by ~50%.

● Multi-Agent RAG Platform: Built a FastAPI backend with an AWS Bedrock (Claude) multi-agent system on MCP architecture handling 10k+ requests/day, and developed additional AI assistants on Bedrock for RAG-based RFP and financial query automation.

● Retrieval & Search: Implemented RAG-based intelligent tool selection using Elasticsearch and vector embeddings, reducing response time by ~40%, and designed scalable Elasticsearch-based chat history storage for large-scale retrieval.

● Real-Time & Session Infrastructure: Built Redis-based session management, checkpointing, and caching for fault-tolerant workflows, and developed real-time streaming chat systems handling thousands of concurrent sessions via SSE.

● LLM Evaluation: Designed and implemented LLM evaluation pipelines using DeepEval to measure hallucination, faithfulness, and answer relevancy in RAG-based AI assistants, and applied LLM-as-Judge techniques to benchmark response quality across multi-agent systems.

● RAG Quality & Retrieval Evaluation: Conducted retrieval-quality evaluation — context precision, recall, and groundedness — across Elasticsearch-based RAG pipelines, optimizing chunking and embedding strategies and improving response consistency.

● API Performance: Optimized high-scale REST APIs using Django REST Framework and FastAPI, improving latency by up to 90%.

● Voice-to-Text Transcription System: Built a real-time speech-to-text application (Python, FastAPI, AssemblyAI API) with asynchronous audio upload, transcription polling, and robust error handling and status tracking across multiple audio formats.

● Developer Tooling & Process: Established a scalable development environment and CI/CD practices for faster release cycles, and introduced AI-assisted development tools (Cursor AI), improving developer productivity by ~30%. EDUCATION

Bachelor of Engineering in Computer Science and Engineering Dec 2020 – May 2024 The Oxford College of Engineering

AWARDS & CERTIFICATIONS

● Claude with AWS Bedrock — hands-on experience with Claude integration, RAG pipelines, and MCP-based orchestration. K M BINDU SHREE

Software Engineer I — Backend & AIML Engineer

**************@*****.*** +91-948******* Bangalore, India linkedin.com/in/k-m-bindu-shree-030b28252 PROFESSIONAL SUMMARY

Backend and AIML Engineer with 2+ years of experience building scalable, high-availability microservices and production-grade Agentic AI systems. Skilled in Python, Django, FastAPI, and distributed system design. Built full-stack AI platforms using LangChain, LangGraph, and MCP-based multi-agent orchestration with RAG pipelines on AWS Bedrock. Experienced in REST API design, system optimization, LLM evaluation, and high-performance backend architectures, achieving up to 90% latency reduction. Familiar with supervised and unsupervised learning techniques, model evaluation metrics, and data processing using NumPy, Pandas, and Scikit- learn.

SKILLS

Languages & Frameworks: Python, Django, Django REST Framework, FastAPI, Next.js Agentic AI & LLM Systems: LangChain, LangGraph, MCP, RAG, AWS Bedrock (Claude) LLM Evaluation & Testing: DeepEval, LLM-as-Judge, Hallucination Detection, Faithfulness & Answer Relevancy, Context Precision/Recall, Regression Testing for LLMs

Data & Infrastructure: MySQL, Redis, Elasticsearch, Docker, Celery, OpenTelemetry, Docling, Git Speech & Audio Processing: Speech-to-Text / ASR, Real-time Transcription, AssemblyAI ML Fundamentals: Supervised & Unsupervised Learning, Regression & Classification Algorithms, NumPy, Pandas, Scikit-learn WORK EXPERIENCE

Software Engineer I Oct 2024 – Present

1Wrk Bangalore

● Agent Orchestrator: Architected and delivered a full-stack, high-availability Agent Orchestrator (Python, LangChain, Redis, Next.js) in a microservices architecture, independently building its Next.js frontend without frontend-team dependencies.

● MCP Architecture & Observability: Designed MCP-based distributed system architecture for modular Agentic AI workflows, including an orchestration layer with OpenTelemetry-based distributed tracing that improved debugging efficiency by ~50%.

● Multi-Agent RAG Platform: Built a FastAPI backend with an AWS Bedrock (Claude) multi-agent system on MCP architecture handling 10k+ requests/day, and developed additional AI assistants on Bedrock for RAG-based RFP and financial query automation.

● Retrieval & Search: Implemented RAG-based intelligent tool selection using Elasticsearch and vector embeddings, reducing response time by ~40%, and designed scalable Elasticsearch-based chat history storage for large-scale retrieval.

● Real-Time & Session Infrastructure: Built Redis-based session management, checkpointing, and caching for fault-tolerant workflows, and developed real-time streaming chat systems handling thousands of concurrent sessions via SSE.

● LLM Evaluation: Designed and implemented LLM evaluation pipelines using DeepEval to measure hallucination, faithfulness, and answer relevancy in RAG-based AI assistants, and applied LLM-as-Judge techniques to benchmark response quality across multi-agent systems.

● RAG Quality & Retrieval Evaluation: Conducted retrieval-quality evaluation — context precision, recall, and groundedness — across Elasticsearch-based RAG pipelines, optimizing chunking and embedding strategies and improving response consistency.

● API Performance: Optimized high-scale REST APIs using Django REST Framework and FastAPI, improving latency by up to 90%.

● Voice-to-Text Transcription System: Built a real-time speech-to-text application (Python, FastAPI, AssemblyAI API) with asynchronous audio upload, transcription polling, and robust error handling and status tracking across multiple audio formats.

● Developer Tooling & Process: Established a scalable development environment and CI/CD practices for faster release cycles, and introduced AI-assisted development tools (Cursor AI), improving developer productivity by ~30%. EDUCATION

Bachelor of Engineering in Computer Science and Engineering Dec 2020 – May 2024 The Oxford College of Engineering

AWARDS & CERTIFICATIONS

● Claude with AWS Bedrock — hands-on experience with Claude integration, RAG pipelines, and MCP-based orchestration. AWS & Cloud: AWS Lambda, Amazon S3, AWS Secrets Manager

Contact this candidate