ATHARVA THAKUR

AI Engineer

https://www.opentalent.in/atharva-thakur

AI Engineer with less than a year in RAG pipelines and multi-agent orchestration.

Key Strengths

Strong foundational knowledge in AI/ML, particularly in LLMs, RAG, and Generative AI, as evidenced by project work and academic pursuits.
Demonstrated ability to architect and implement complex AI systems, including RBAC-enforced RAG platforms and multi-agent reasoning systems.
Proficiency in developing production-grade AI backends using FastAPI, incorporating features like asynchronous processing, caching, rate limiting, and observability.
Experience with advanced NLP techniques, including fine-tuning large language models (RoBERTa-base) and integrating commercial LLMs (LLaMA 3.1-8B).
Focus on evaluation-driven development, with concrete metrics cited for answer relevance, faithfulness, confidence calibration, and model accuracy.
Practical experience with MLOps concepts such as P95 tracking, safety guardrails, and real-time analytics dashboards.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects demonstrate a proactive and innovative approach to AI challenges, aligning well with a culture that values research, development, and practical application of cutting-edge technologies. The diversity of projects (RAG, multi-agent systems, NLP for customer support) indicates adaptability and a broad interest in AI applications. The pursuit of a Master's degree in AI while actively building complex systems shows a commitment to continuous learning and growth.

Soft Skills & Operational Fit

The candidate's project descriptions indicate a strong problem-solving aptitude and a methodical approach to system design and evaluation. The emphasis on 'measurable, reliable, and production-minded' AI systems suggests a good operational fit for roles requiring robust and maintainable solutions. The detailed project descriptions also imply strong written communication skills.

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Projects

Intelligent Customer Support Ticket Routing System

June 1, 2026 – Present

Fine-tuned RoBERTa-base on 12K customer support tickets across 8 intent classes, achieving 75% accuracy and 0.76 macro F1; replacing rule-based keyword routing with async multi-intent inference. Built an asynchronous FastAPI backend with Redis caching, confidence-threshold routing, IP-based rate limiting (10 requests/min), and latency observability with P95 performance tracking. Integrated Groq-hosted LLaMA 3.1-8B for intent-aware response generation, implementing safety guardrails for PII protection, payment-retry prevention, and sentiment-based ticket prioritization. Developed a real-time Streamlit analytics dashboard featuring intent distribution, confidence-score monitoring, and latency analytics for operational insights.

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Sentinel: RBAC Secured RAG System

June 1, 2026 – Present

Architected an RBAC-enforced RAG platform with access control applied directly at the Qdrant vector layer, ensuring unauthorized document chunks never reached the LLM. Engineered a multistage retrieval pipeline leveraging HyDE, hybrid BM25+dense retrieval, RRF fusion, and cross-encoder reranking, achieving 93.8% Answer Relevance. Achieved 87.7% Faithfulness through LLM-as-a-Judge evaluation, leveraging confidence-calibrated decision gating and sliding-window context expansion to reduce unsupported responses. Designed a confidence-calibrated decision gate (hard-hit, soft-hit, no-info) achieving 100% gate accuracy while eliminating unnecessary LLM calls on low-confidence retrievals.

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Delphi: Self-Improving Multi Agent Decision Intelligence Platform

June 1, 2026 – Present

Architected a LangGraph-based multi-agent reasoning system that dynamically assembles 4-8 specialized expert councils per query using dual-model inference and debate-driven decision making. Engineered self-healing agents with ELO-based reputation tracking and recovery mode, injecting historical failure/success lessons and enforcing domain-specific self-critique for underperforming experts. Evaluated the framework across 75 simulated domain scenarios, improving confidence calibration by +0.25 while maintaining stable expert reputation dynamics (4.66 ELO drift). Built an asynchronous FastAPI backend with aiosqlite persistence and validated system reliability through 45 unit and integration tests using isolated in-memory databases.

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Key Strengths

Strong foundational knowledge in AI/ML, particularly in LLMs, RAG, and Generative AI, as evidenced by project work and academic pursuits.
Demonstrated ability to architect and implement complex AI systems, including RBAC-enforced RAG platforms and multi-agent reasoning systems.
Proficiency in developing production-grade AI backends using FastAPI, incorporating features like asynchronous processing, caching, rate limiting, and observability.
Experience with advanced NLP techniques, including fine-tuning large language models (RoBERTa-base) and integrating commercial LLMs (LLaMA 3.1-8B).
Focus on evaluation-driven development, with concrete metrics cited for answer relevance, faithfulness, confidence calibration, and model accuracy.
Practical experience with MLOps concepts such as P95 tracking, safety guardrails, and real-time analytics dashboards.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

ATHARVA THAKUR

Key Strengths

Cultural & Operational Fit

About

Top Skills

Skills

Education

Projects

Key Strengths

Cultural & Operational Fit