great problem solving skills.
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Assessing your cultural and operational fit
Legacy-System-AI-Integration-Bridge
June 1, 2026 – Present
Build a middleware layer that connects an LLM to a real legacy system (CRM, ERP, SQL database) through a secure API layer. 80% of FDE work is breaking through the "integration wall" — this project proves you can do exactly that.
View ProjectEnterprise-AI-Support-Agent-with-Eval-Suite
June 1, 2026 – Present
A production-grade multi-tenant RAG agent that ingests a company's docs, answers customer queries, and ships with a full LLM-as-judge evaluation harness. This directly mirrors what FDEs build for enterprise clients every day.
View ProjectDomain-specific-LLM-fine-tuning-deployment-pipeline
May 7, 2026 – Present
Take a 7B open-source model (Mistral or LLaMA-3) and fine-tune it on a specific domain dataset (e.g. Indian legal documents, medical QA, or code review feedback) using QLoRA on a free GPU. Build an end-to-end MLOps pipeline: data prep → fine-tune → evaluate → serve via REST API — with automated benchmarking against the base model.
View ProjectSelf-correcting-RAG-research-assistant
May 7, 2026 – Present
Build a research assistant that ingests a custom document corpus (PDFs, URLs), retrieves relevant chunks via semantic + BM25 hybrid search, and automatically detects when retrieved context is insufficient — then re-queries with a refined question before answering. Every response includes a factual confidence score.
View ProjectLLM-Inference-Serving-System
April 13, 2026 – Present
A minimal but production-aware LLM inference server implementing continuous batching, block-based KV cache management, and speculative decoding — achieving 3.4x higher throughput than naive batching on mixed-length requests.
View ProjectCultural Fit Analysis
The candidate's project portfolio is heavily focused on personal initiatives, demonstrating strong self-motivation and a passion for AI/ML. The projects align well with the target role of Data Scientist, particularly those with a focus on applied machine learning and LLMs. However, the lack of team-based projects or formal work experience makes it challenging to assess collaboration skills or adaptability within a structured organizational culture. The diversity of projects, while all AI-focused, shows a breadth of interest within the LLM domain.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and self-driven individual with a strong interest in cutting-edge AI/ML technologies. The focus on production-aware systems and enterprise integration suggests an understanding of practical application and operational challenges. However, without formal work experience or psychometric test results, it is difficult to assess specific soft skills like teamwork, stress handling, or communication clarity in a professional setting.