☕ Senior Java Engineering Professional | 🏗️ Transitioning into Software Architecture | 🤖 AI Engineering | 🔬 AI Research

Senior Java engineering professional transitioning toward Software Architecture, with AI engineering skills and AI research experience.· 📍 Pune, India

• Design and contribute to scalable, secure, and observable enterprise applications and distributed systems
• Design microservices, APIs, event-driven architectures, and enterprise integration solutions
• Modernize Java platforms using Spring Boot, resilient architecture, and cloud-native patterns
• Apply security, performance, scalability, and reliability practices to production systems
• Build AI applications using RAG, agentic workflows, multi-agent systems, evaluations, and guardrails
• Work with context engineering, MCP, local LLMs, fine-tuning, and production AI deployment
• Conduct independent research on the reliability, monitoring, and repair of LLM agents
• Lead technical design, code reviews, stakeholder collaboration, troubleshooting, and root-cause analysis

Real-Time Detection and Repair of LLM Agent Failures

A lightweight research system for detecting when an autonomous LLM agent begins to derail during execution and initiating repair before the failure propagates.

• Learns normal execution dynamics from healthy agent trajectories
• Combines reservoir computing, anomaly detection, and sequential monitoring
• Uses behavioral, semantic, uncertainty, metadata, and grounding signals
• Designed for real-time, CPU-only monitoring at approximately 200 µs per agent step
• Evaluated across multiple agent frameworks, models, tasks, and failure types

I am extending my Enterprise Java engineering background into production-oriented AI engineering through hands-on learning, projects, and independent research across:

• LLM fundamentals, transformers, embeddings, and semantic similarity
• Retrieval-Augmented Generation: vector, hybrid, SQL, graph, and multimodal RAG
• Vector databases, document parsing, chunking, reranking, and semantic search
• Tool-calling agents, ReAct workflows, memory, routing, and multi-agent orchestration
• LangChain and LangGraph application development
• RAG and agent evaluation, guardrails, observability, and AI security
• Context engineering and Model Context Protocol (MCP)
• Fine-tuning with LoRA/QLoRA, synthetic-data pipelines, SLMs, and local inference
• FastAPI-based services, model serving, caching, routing, scaling, and cost optimization
• Independent research in lightweight runtime monitoring and repair of LLM-agent failures

Cloud and container technologies above include professional exposure and collaboration with DevOps teams; they are not presented as my primary specialization.

• Distributed systems and microservices
• API and enterprise integration architecture
• Event-driven architecture and asynchronous messaging
• Resilience, fault tolerance, and graceful degradation
• Application security, authentication, and secrets management
• Performance engineering, scalability, and database optimization
• Observability, runtime monitoring, and distributed tracing
• Legacy modernization and cloud adoption
• Production-oriented design of AI-powered systems

• M.S. in Electrical Engineering — Texas A&M University–Kingsville, US
• B.E. in Electronics and Telecommunication Engineering — SRTMU, India
• Certificate Programme in Project Management — IIM Indore, India

• Architecture should simplify change, not merely organize complexity
• Production reliability matters more than impressive prototypes
• AI systems need evaluation, observability, security, and failure handling
• Every component should justify its operational cost
• Good systems are secure, explainable, maintainable, and resilient by design

“Years of engineering teach you how to build software.
Architecture teaches you how to make the right systems.
AI is changing what those systems can become.”