AI/ML Engineer specializing in Agentic Architectures, Production RAG systems, and LLM Observability. Proven track record of building autonomous multi-agent pipelines, real-time feedback loops, and highly scalable backend infrastructure. Strong foundation in machine learning, grounded in first-principles execution—from orchestrating distributed, stateful LLM deployments to building zero-shot machine unlearning frameworks. Passionate about engineering reliable, deterministic, and tightly monitored GenAI solutions that solve complex enterprise challenges.
Master's in Data Science — Defence Institute of Advanced Technology (DIAT), Pune
Bachelor's in Mathematics — Indira Gandhi National Open University (IGNOU), New Delhi
| Aug 2025 Jan 2026 |
AI Engineer — Evntro · Observo Tech Studio • Contributed to a production-grade agentic B2B outreach system integrating email sending, receiving, replying, and campaign-level tracking across multiple mailboxes • Designed and implemented the LLM tool layer (send/track/reply) with persistent conversation state in PostgreSQL, enabling autonomous, multi-threaded communication workflows • Engineered a real-time feedback pipeline capturing 16+ delivery and engagement signals, enabling adaptive, data-driven campaign optimization • Developed a context-aware RAG pipeline leveraging prospect data for personalized email generation, improving relevance over template-based outreach • Refactored the FastAPI backend to resolve critical issues and deployed services on Azure Function Apps, enabling a scalable, event-driven system |
| Apr 2025 Jul 2025 |
GenAI Engineer (R&D) · Confedo AI • Built multi-stage RAG pipelines and agentic workflows to simulate real-world LLM usage and enable systematic evaluation of faithfulness, relevance, and failure modes in complex pipelines • Designed advanced retrieval pipelines (query routing, HyDE, reranking) to generate challenging, multistep queries, improving robustness and realism of evaluation benchmarks • Reverse engineered 3 LLM observability frameworks to understand tracing architectures and extract design insights for building in-house evaluation and observability tooling • Built a PoC for an in-house LLM tracing system to validate design decisions and inform the product team in developing a production-grade tracing SDK |
| Jan 2025 Jun 2025 |
Research Intern · Usable Security Group (USG) Lab @ IIIT Delhi • Designed and validated a data-free, zero-shot machine unlearning framework to enable class-level data removal without requiring access to original training data, supporting privacy compliance (e.g., GDPR) • Leveraged contrastive model inversion to generate 5K synthetic samples, enabling effective targeted unlearning while preserving model performance on unaffected classes • Achieved 90%+ unlearning efficacy across 8 classes on the SVHN dataset, while maintaining 95%+ accuracy on retained classes, minimizing unintended knowledge loss • Improved unlearning efficiency with a 77% reduction in runtime compared to the GKT baseline, enhancing feasibility for real-world deployment • Conducted comparative evaluation against 4 baseline methods (EMMN, GKT, zMuGAN, retraining) and explored subset/coreset strategies to optimize scalability and effectiveness of unlearning |
| Languages |
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| ML / DL |
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| LLM / RAG |
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| Backend |
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| Cloud |
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| Data |
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Enterprise-grade Multi-Tiered Retrieval-Augmented Generation System. Integrates dynamic fallback strategies and diverse retrieval architectures to ensure high-fidelity context extraction across complex datasets.
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Custom-built, local-only LLM tracing and observability tooling. Engineered for async agentic workflows using
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Multi-path autonomous RAG pipeline featuring intelligent query routing, self-corrective retrieval loops, and iterative web search fallbacks to actively detect and eliminate hallucinations in real time.
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On-device RAG engineered around LEANN's graph-based search. Indexes millions of documents locally in gigabytes without significant accuracy loss, achieving zero cloud dependency and strict privacy.
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Structure-aware documentation RAG pipeline built from scratch. Crawls 104 FastAPI pages, preserves section hierarchy, generates 971 metadata-rich chunks, and delivers citation-grounded answers via Pinecone + Groq.
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RAG system with SHA-256 document deduplication, namespace isolation, semantic reranking, and inline-cited answers. Upload a 200-page manual, ask a question, get a precise cited response.
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> Engineering multi-tiered, self-correcting Agentic RAG architectures
> Developing lightweight, local-only LLM observability and tracing SDKs
> Exploring hardware-efficient, vectorless RAG approaches (PageIndex & LEANN)
> Scaling distributed, stateful LLM tool layers for autonomous workflows
