Ebin B.

Ebin B.

+•• ••••• •••••••••••@•••••.com

AI Engineer — Independent / Open Source · 4.1 yrs

As an independent AI Engineer with nearly five years of experience, I am driven by a logical and ambitious pursuit of mastery in developing robust and safe AI systems. My creative approach to problem-solving, combined with a pragmatic focus on experimental design and benchmarking, allows me to build innovative solutions, particularly within the open-source community.

  • Kerala, India
  • Open to Bangalore · Bombay
  • Remote, On-site, Hybrid

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Skills

Claimed

User Behavior SimulationQuestion Generation for AI Model TestingQN ModelsImplementing AI Model BiasesLLM InterrogationAI Environment EngineeringResearch Sprint ParticipationIterative Problem SolvingModel ComparisonSoftware ConsultingMLflow UsageLLM Token Budget Management

Proof of Experience

AI Engineer — Independent / Open Source

Mar 2026 – Present

  • Building audit-grade AI agents and evaluation tooling in the open.
  • Every number below regenerates from a committed script at github.com/ebt55.
  • • diffing-agent-bench: sealed, preregistered benchmark for black-box model-diffing agents.
  • Five LoRA finetunes of Qwen3.5-9B, answer key committed before any run, audited blind by a published recipe and four cheaper conditions.
  • The agent asked the decisive database question in 0 of 13 attempts; a $0.15 prompt battery found the plant against $3.14 per agent run.
  • MATS 12.0 work sample.
  • • odd-number-forensics: forensic study of a published reward-hacking environment, ~5,300 audited samples.
  • One model swings 0% to 87% gaming on single-line prompt edits.
  • Falsified predictions published with the confirmed ones.
  • • ProofPack: pre-approval review agent for Medicaid-audited disability programs.
  • Every "Found" cites a SHA-256-hashed screenshot, quotes must match page text verbatim, and hashes are written by code the model never touches. $0.02–$0.19 per review, ~3 min vs 20–40 by hand, 87 offline tests.
  • • IncidentGate: lab for policy gates, an action monitor and single-use human-approval tokens over an incident agent. 594 kill points, 434/434 recoveries, zero duplicate mutations.
  • Closed at a baseline Sep 2026; on one scenario the full chain matched no safeguards at all, 12 side effects.
  • • Digital Grimace Scale: preregistered Apart Research study.
  • Primary test failed and is published as such; a second frozen plan found an effect (p = 0.005) that DPO training removed from the language while the behaviour stayed.
  • • ExactDoc: PDF to editable DOCX, verified by render-back and word-position diffs. 16/16 corpus, 95.9% text kept live, 99 unit + 83 gate-mutation tests.
  • • whose-voice: blind attribution of hidden principals in poisoned training data (Apart × Formation hackathon). 12–44% top-1 of 47 candidates, chance 2.1%, with a map of where the method breaks.

Software Engineer — Python Backend & Applied AI

Zackriya Solutions · Jan 2022 – Jun 2025

  • Worked as a Python backend engineer at a service company building MVPs, prototypes, data tools, and applied AI systems for startup clients across the US, Canada, Europe, and Australia.
  • Core responsibilities:
  • • Built FastAPI/Python backends for AI, data, and workflow automation products.
  • • Designed backend architecture for 0→1 MVPs: API structure, data schemas, integrations, and deployment flows.
  • • Integrated LLM APIs, vector databases, speech APIs, cloud services, and data pipelines.
  • • Worked directly with ambiguous client requirements and translated them into functioning backend systems.
  • Selected projects:
  • • Speech assessment backend: processed short candidate videos using Whisper and Microsoft Pronunciation APIs; deployed on AWS Fargate.
  • • Natural-language search: built an LLM-to-SQL backend for real-estate search using GPT-3.5, Cloud SQL, and GCP Cloud Run.
  • • Document search/RAG: built ingestion and vector search flows using Qdrant over 1000+ documents.
  • • Healthcare/device tools: built Python scripts and small GUI/debug tools for healthcare/device-adjacent projects.
  • • IoT data handler: consumed MQTT streams and inserted live sensor data into MongoDB.
  • • Report generation: built a Python API to generate complex PDF reports with nested tables and diagram images.
  • • LLM infra prototype: fine-tuned Mistral 7B and served inference using vLLM/GKE as a prototype/learning project.
  • Tech: Python, FastAPI, Docker, PostgreSQL, MongoDB, SQL, Qdrant, OpenAI APIs, Whisper, AWS Fargate, GCP Cloud Run, GitHub Actions, pytest.

Proof-of-Work

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