Aman Jha

AI Engineer — agents, retrieval & fine-tuning

AI Engineer focused on building production-grade LLM and agentic AI systems, with experience in long-term agent memory, retrieval pipelines, and domain-specific model fine-tuning. Experienced in taking AI systems from research and experimentation to deployable, production-ready services.

experience:

Founder & Lead Engineer at thehodl.fun

September 2025 to February 2026
  • Built and shipped a cross-chain token launchpad on Push Chain from zero to production, owning architecture, smart contracts, backend, and frontend
  • Solved a core DeFi UX problem (token launches trapped on a single chain) by enabling launches and trades with assets from any supported chain (ETH, SOL), no bridging required, via Push Chain's universal account system
  • Scaled to ~800K transactions and 500K+ token launches on testnet
  • Wrote the bonding-curve engine into a modular, upgradeable system: separate Core orchestrator, Factory, per-token BondingCurve, and an ERC4626 FeeVault for fee management, with proper constant-product AMM math and virtual reserves for smoother price discovery
  • Integrated Uniswap V3 for automatic DEX listing once a token's bonding curve hits its liquidity target, graduating it from the internal curve to full open-market trading
  • All contracts upgradeable via UUPS proxy pattern, with reentrancy protection, access control, and invariant checks
  • Shipped a full-stack system: Foundry smart contracts, NestJS backend, Next.js frontend
  • Runner-Up, Project G.U.D hackathon (Push Chain)
  • Source on GitHub
SolidityFoundryTypeScriptNext.jsethers.jsUniswap V3

projects:

MetaMemory

  • Persistent memory infrastructure for AI agents, scoring 89.0% task-averaged accuracy on LongMemEval-S (84.4% overall, 500-question long-term memory benchmark), evaluated with an LLM-as-judge Gemini reader
  • Retrieval pipeline combining semantic, temporal, BM25 keyword, and reranking channels, merged with Reciprocal Rank Fusion and re-weighted by a rule layer that learns from outcomes logged on prior queries
  • CABAR, a context-budget-aware retrieval pipeline for long-context agents: greedy token allocation, lexical MMR, recency weighting, and extractive summarization — cutting context usage by ~72-76% in internal evaluations at equal benchmark accuracy
TypeScriptPostgreSQL (pgvector)RedisNeo4jOpenAIGemini

DRT — Decomposed Relevance Tensors

  • Engineered and benchmarked an alternative retrieval-scoring architecture — splitting each embedding into k=6 weighted sub-vectors instead of one flat similarity score — against a cosine-similarity baseline trained on the full MS MARCO dataset (8.84M documents, 6,980 queries)
  • Ran a 4-step diagnostic (scoring ablations, per-slot logistic probes, failure analysis, linear CKA) to isolate why it underperformed baseline by 2% MRR@10, tracing the cause to encoder representation drift (CKA 0.945) rather than a broken scoring function
  • Open-sourced the full pipeline — training, evaluation, and diagnostics — with all raw results and logs checked in for reproducibility
PythonPyTorchMS MARCO

Sealevel

  • Coding LLM fine-tuned specifically for Solana/Anchor development: QLoRA fine-tune of Qwen2.5-Coder-7B on 270k Solana-specific records
  • 85% on an 80-task Solana/Anchor benchmark, 90% on general HumanEval
  • RAG pipeline over current Solana docs (Qdrant) keeps it accurate as Anchor syntax evolves
  • Served via an OpenAI-compatible API across web, CLI, VS Code, and MCP, with direct integration into Claude Code, Cursor, and Windsurf
  • 1,500+ installs on PyPI
PythonPyTorchSGLangQdrantMCPNext.js

Orky

  • Enterprise agentic AI platform that orchestrates workflows across 17 live app clients (ServiceNow, AWS, Jira, Salesforce, SAP, Snowflake, SharePoint) via a function-calling loop where Gemini selects and chains real API actions, with progress streamed over SSE
  • Enforces role-based access control at query time, filtering RAG results against each user's designation before content reaches the LLM, so employees only retrieve answers from HR, IT, CRM, and compliance sources they're authorized to see
  • Multi-step workflow engine that sequences AI agents with variable passing between steps, mid-workflow file-upload pausing, and automatic email notification on completion
FastAPINext.jsGoogle GeminipgvectorPostgreSQLAWS

achievements:

7 wins across grants and hackathons

Grants

  • Solana Foundation × CoinDCX India Grant: $10,000 for Lavinth (May 2025)
  • Solana Foundation × CoinDCX India Grant: $4,000 for tradeX (Feb 2025)
  • Dialect Instagrant: $1,000 for X-Mailer (Jan 2025)

Hackathons

  • Agents in Action Hackathon: 🥉 Akash Track (Best Overall Project), $2,000 Akash Credits, for PayPerUse
  • Agent Arena – University Hackathon: IQAI MVP Winner, for ResearchOS
  • Zo House Hackathon: Data Management AI Agent Track, 420 USDT, for DockMind
  • Project G.U.D (Push Chain): Runner-Up, for Hodl.fun

blog:

i build with:

PythonTypeScriptPyTorchQLoRA Fine-tuningRAG PipelinesOpenAIGoogle GeminiQdrantPineconepgvectorNeo4jRedisPostgreSQLFastAPIHonoNext.jsMCPSGLangDockerAWS

if you're hiring for AI/ML engineering, or want to build agents, retrieval systems, or fine-tuned models together, feel free to dm me on x or drop me an email.