AI Engineer
I work across Generative AI, RAG, computer vision, and Python backend systems. My projects focus on practical engineering problems: grounding LLM outputs in real evidence, verifying generated answers with deterministic tools, and building video analytics pipelines that remain useful outside controlled environments.
0 days
About
I started with applied computer vision work, building and evaluating object-detection pipelines for architectural and engineering use cases. That experience taught me that a model performing well on a test set is not automatically ready for real-world use. Since then, I have expanded into Generative AI systems, RAG pipelines, structured LLM workflows, and Python backend development. I am particularly interested in systems that reduce unreliable model behavior through grounded retrieval, deterministic verification, structured outputs, and transparent review workflows. My work currently spans three areas: evidence-grounded GenAI applications, verified LLM workflows, and real-time computer vision pipelines.
Experience
NDSoftTech Solutions · India
Jan 2023 - Jun 2023
Contributed to software engineering tasks across backend implementation, debugging, and application support.
Freelancer / Independent Projects · Remote
Jul 2023 - Present
Built applied AI prototypes and MVPs across RAG, OCR, computer vision, and LLM-backed workflows.
Neilsoft · India
Jan 2024 - Present
Worked on ML and AI engineering tasks involving model evaluation, backend integration, and applied automation.
Projects
A recruiter-friendly view of applied work across RAG, computer vision, OCR, LLM verification, and backend integration.
A RAG-based resume-tailoring system that generates role-specific resumes from verified user evidence using structured retrieval, deterministic validation, and human review.
Evidence-grounded generation with human review
A warehouse video-analytics pipeline for parcel-condition monitoring, OCR-assisted metadata extraction, movement tracking, and incident review.
92.7% precision
An LLM and SymPy-based math-reasoning pipeline that independently verifies generated answers before presenting them to users.
Independent symbolic verification before response
Skills
Resume
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