Puneet Singhal
AI Solutions Architect
Expert in building scalable systems and enterprise grade solutions
14+ years of experience specializing in AI, machine learning, and LLM customization with OpenAI GPT, Anthropic Claude, and Google Vertex AI. Expert in building intelligent chatbots, semantic search engines, and AI-driven analytics platforms using Python, AWS, and modern DevOps practices.
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puneetsinghal.11@gmail.com
Core Competencies
14+ years of expertise across full-stack backend development, cloud architecture, and AI integration
Languages & Frameworks
Databases
Message Streaming
Cloud & DevOps
AI & LLM Integration
Monitoring & Logging
Soft Skills
Industry Experience
Professional Journey
Over the past 14+ years I’ve evolved from building Java microservices to architecting agentic AI systems at scale. Here’s a snapshot of the roles, impact, and platforms I’ve shaped along the way.
Career Evolution
Backend Development
Enterprise Java, Spring Boot, RESTful APIs, and backend systems architecture
Microservices Architecture
Distributed systems design with microservices, scalability, and integration
Event-Driven Systems
Apache Kafka, event streaming, message queues, and asynchronous processing
Cloud Architecture
AWS services, Kubernetes orchestration, Docker, and CI/CD automation
AI Integration
LLM integration, NLP models, and AI-driven intelligent systems
Agentic AI Systems
LangGraph workflows, multi-agent orchestration, and advanced LLM integration
Software Development Company | Backend Developer
2011 - 2014
- ▸Developed enterprise-level backend systems using Java and Spring Framework
- ▸Implemented RESTful APIs and microservices for scalable applications
- ▸Designed robust backend architectures and database integration
- ▸Collaborated with cross-functional teams to deliver high-quality solutions
Enterprise Solutions Provider | Sr. Backend Engineer
2014 - 2018
- ▸Architected microservices-based backend systems using Spring Boot
- ▸Implemented RESTful APIs and integrated third-party services
- ▸Optimized application performance and database query efficiency
- ▸Mentored junior developers and established coding best practices
Healthcare - Employee Benefits | Solutions Architect
Mar 2018 - Oct 2021
- ▸Designed scalable microservices architecture using Java Spring Boot for benefits administration
- ▸Integrated Apache Kafka for real-time data streaming and event-driven architecture
- ▸Implemented Cassandra for distributed, fault-tolerant data models supporting high-availability systems
- ▸Built RESTful APIs optimized for performance, security, and scalability
- ▸Orchestrated containerized microservices using Docker and Kubernetes across cloud environments
Workiva - SP Team | Solutions Architect
Nov 2021 - Apr 2025
- ▸Designed and implemented high-scale microservices for notifications, scheduling, and EDI file processing
- ▸Architected event-driven messaging using Apache Kafka for reliable, high-throughput data streaming
- ▸Engineered Kubernetes orchestration on AWS EKS with Docker containerization for production deployments
- ▸Implemented CI/CD pipelines using Jenkins and AWS CodePipeline for automated build, test, and deployment
- ▸Established comprehensive monitoring using Splunk, Prometheus, and Grafana for real-time observability
AI-Based Industry Classification System | Sr. AI Engineer
Jan 2025 - Mar 2025
- ▸Developed custom NLP models using Claude Sonnet 3.5 v2 for automated business classification into NAICS, SIC, and ISIC codes
- ▸Implemented transfer learning with pre-trained language models for improved contextual understanding and accuracy
- ▸Designed RESTful API supporting thousands of classification requests per second using AWS Lambda and DynamoDB
- ▸Built confidence-scoring mechanism with multi-model classification approach for enhanced accuracy
Multi-agent Conversational AI Application | Sr. AI Engineer
Mar 2025 - Present
- ▸Architected LangGraph-based workflow system with 4 specialized nodes for intelligent routing and context management
- ▸Integrated OpenAI GPT-4 and GPT-5-mini models with sophisticated prompt engineering for 6 specialized TVET assistants
- ▸Designed microservices architecture with Docker Compose orchestration and PostgreSQL for conversation persistence
- ▸Implemented JWT-based authentication with role-based access control and comprehensive error handling
Featured Projects
Showcasing LLM integration, LangChain orchestration, and enterprise automation solutions
Multi-Agent Conversational AI System
Mar 2025 - PresentSophisticated TVET educational platform using LangGraph and OpenAI Assistants API. Orchestrates 6 specialized AI assistants (TPP Orchestrator, My Pedagogy, My TVET Practice, My Worldview, Reflective Practitioner, Constructive Alignment) with intelligent routing and context-aware conversations. Industry: Education/TVET. Tech: LangGraph, OpenAI Assistants API. Outcome: context-aware multi-agent orchestration.
Technologies
6 Specialized Assistants
AI-Based Industry Classification System
Jan 2025 - Mar 2025Automated classification of businesses into NAICS, SIC, and ISIC codes using Claude Sonnet 3.5 v2 and NLP. Features real-time API, confidence scoring, multi-model classification, and self-learning feedback loop for continuous improvement. Industry: Data/Analytics. Tech: Claude Sonnet 3.5, AWS Lambda, DynamoDB. Outcome: real-time classification with confidence scores.
Technologies
Real-time Classification API
Fintech Conversational AI
1 YearReal-time conversational AI integrated with mobile app using FastAPI and WebSockets. Leverages LangChain Agent framework with Gemini LLM and Structured Tools for accessing user profiles, financial goals, transactions, and account metadata with sub-100ms latency. Industry: FinTech. Tech: FastAPI, WebSockets, LangChain, Gemini. Outcome: sub-100ms chat UX.
Technologies
Sub-100ms Latency
Notifications Service - Workiva
Nov 2021 - Apr 2025High-scale microservice for bulk notifications across Email, Slack, and Microsoft Teams. Event-driven architecture using Apache Kafka with Docker/Kubernetes deployment on AWS EKS. Validated for 10K+ concurrent users using Locust performance testing. Industry: Enterprise SaaS. Tech: Java, Spring Boot, Kafka, AWS EKS. Outcome: 10K+ concurrent users.
Technologies
10K+ Concurrent Users
Schedule Service - Workiva
Nov 2021 - Apr 2025Sophisticated scheduling microservice built with Kotlin and OpenAPI for time-based workflows and automated task execution. Features JobRunr for distributed background processing, Confluent Kafka for event-driven architecture, and support for recurring jobs, cron triggers, and conditional execution. Industry: Enterprise SaaS. Tech: Kotlin, JobRunr, Confluent Kafka. Outcome: exactly-once processing.
Technologies
Exactly-Once Processing
Healthcare EDI System
Mar 2018 - Oct 2021HIPAA-compliant EDI file generation system with microservices architecture. Features carrier profile configuration, custom field support, and automated file transmission via FTP/SFTP/Email. Includes EBA-EDI integration, file generation, carrier profile, and report generation services. Industry: Healthcare. Tech: Spring Boot, JPA, RabbitMQ. Outcome: HIPAA-compliant EDI automation.
Technologies
HIPAA Compliant
Legal Case Management System
2 YearsEnterprise-level legal case management platform with document automation, client portal, and billing integration. Features intelligent document generation, contract management, case timeline tracking, and automated billing workflows. Built with microservices architecture for scalability. Industry: Legal Tech. Tech: Spring Boot, React, Elasticsearch. Outcome: enterprise document automation.
Technologies
Enterprise Legal Platform
Mobile App Security Platform
1 YearComprehensive mobile application security testing and monitoring platform. Features static and dynamic code analysis, vulnerability scanning, penetration testing automation, and real-time threat detection. Supports iOS, Android, and hybrid app security assessment with CI/CD integration. Industry: Security. Tech: FastAPI, Kubernetes, OWASP. Outcome: automated SAST/DAST.
Technologies
Automated Security Testing
API Gateway & Rate Limiting Service
6 MonthsHigh-performance API gateway with intelligent rate limiting, authentication, and request routing. Features distributed rate limiting using Redis, OAuth 2.0 integration, request throttling, circuit breaker pattern, and real-time analytics. Handles millions of requests per day with sub-millisecond latency. Industry: Infrastructure. Tech: Go, Redis, OAuth 2.0. Outcome: sub-millisecond latency at scale.
Technologies
High-Scale API Gateway
Employee Benefits Administration Platform
Mar 2018 - Oct 2021Comprehensive benefits administration system for employer groups with configurable benefit plans, eligibility management, and enrollment workflows. Features multi-tenant architecture, role-based access control, automated notifications, and integration with carrier systems via EDI transactions. Industry: Employee Benefits. Tech: Cassandra, Kafka, Microservices. Outcome: scalable multi-tenant platform.
Technologies
Large-Scale Benefits Platform
Intelligent Document OCR & Processing System
6 MonthsAdvanced OCR system with AI-powered document recognition, text extraction, and data structuring. Built with Tesseract OCR, image preprocessing, and natural language processing. Features support for multiple document types (PDFs, images, scanned documents), table extraction, and automated data validation with ML-based accuracy scoring. Industry: Automation. Tech: Tesseract, OpenCV, NLP. Outcome: multi-format OCR at scale.
Technologies
Multi-Format Document Processing
Education & Certifications
Academic foundation and professional certifications
Academic Degrees
Master of Business Administrator
IT & Finance
Rajasthan Technical University
Bachelor of Technology
Computer Science
Rajasthan University
Professional Certifications
AWS Cloud Solutions Architect Associate
Amazon Web Services
Zend Certified Engineer
Zend Technologies
Frequently Asked Questions
Answers to common questions about LLM integration, agentic AI, and semantic search.
How do you design production-grade agentic AI systems with LangGraph?
How do you design production-grade agentic AI systems with LangGraph?
LangGraph excels at stateful, multi-step workflows with cycles and human-in-the-loop. Define nodes as agent actions, use conditional edges for routing, implement checkpointing for recovery, and leverage sub-graphs for modular agent teams. Add interrupt points for approval gates and use streaming for real-time feedback.
What's the difference between function calling and tool use in modern LLMs?
What's the difference between function calling and tool use in modern LLMs?
Function calling (GPT-4o, Claude 3.5) returns structured JSON for your code to execute. Tool use (Anthropic's paradigm) treats tools as first-class with retries and error handling. Always validate tool outputs, implement timeouts, and log tool chains for debugging. Prefer parallel tool calls when dependencies allow.
How do you orchestrate multi-agent systems for complex enterprise workflows?
How do you orchestrate multi-agent systems for complex enterprise workflows?
Use supervisor patterns for routing, specialist agents for domain tasks, and shared memory (vector stores or state graphs) for context. Implement handoffs with clear responsibilities, add circuit breakers for agent failures, and use semantic routing (embeddings) over rigid conditionals. Monitor token usage per agent.
What are the critical production concerns for RAG systems in 2025?
What are the critical production concerns for RAG systems in 2025?
Implement hybrid search (sparse + dense), semantic caching for repeated queries, and reranking (Cohere, cross-encoders). Handle multimodal docs (PDFs with images), use metadata filtering aggressively, and implement incremental updates. Monitor retrieval precision, chunk relevance, and add user feedback loops for ground truth.
How do you handle LLM observability and debugging in production?
How do you handle LLM observability and debugging in production?
Use LangSmith, Weights & Biases, or Helicone for full trace logging. Track prompt versions, model outputs, latency P95/P99, and cost per request. Implement structured logging with trace IDs, monitor for hallucinations with eval datasets, and set up alerting for quality degradation. Log refusals and edge cases separately.
Which LLM should you choose for specific production use cases?
Which LLM should you choose for specific production use cases?
GPT-4o for general reasoning and speed, Claude 3.5 Sonnet for long context and code, Gemini 2.0 Flash for multimodal and low latency, Llama 3.x for on-prem compliance. Use smaller models (GPT-4o-mini, Haiku) for classification and routing. Always benchmark on your domain data.
How do you implement semantic caching and prompt optimization at scale?
How do you implement semantic caching and prompt optimization at scale?
Cache embeddings for repeated queries with cosine similarity thresholds (0.95+). Use prompt compression techniques (LLMLingua), template few-shot examples strategically, and version prompts with A/B testing. Implement token budgets per request class and use streaming to reduce perceived latency.
What's the best practice for managing agent memory and context windows?
What's the best practice for managing agent memory and context windows?
Use tiered memory: short-term (conversation buffer), medium-term (vector store for session), and long-term (indexed past interactions). Implement sliding windows with summarization for long conversations. For LangGraph, leverage checkpointing for persistence. Prune irrelevant context with relevance scoring.
Let's Connect
Interested in discussing new opportunities, collaborations, and innovative backend systems and AI-powered solutions