Applied AI engineer — agent orchestration, RAG, evals, and LLM inference. I build agentic systems and the inference and evaluation infrastructure underneath them, then publish the measurements — including the ones that came out negative.

At BNY Mellon (Applied AI Engineer, 2024–present) I ship RAG assistants, agentic workflows, MCP servers, and evaluation harnesses on a Python/Go distributed-systems foundation.

→ Interactive portfolio · heman10x@gmail.com

Open to remote roles: Applied AI Engineer · Fullstack AI Engineer · Forward Deployed Engineer · Inference / Model Performance Engineer. Based in India, 4–6h overlap with EU and US-East.

Python Go TypeScript · agent orchestration multi-agent systems RAG hybrid retrieval context engineering evals LLM guardrails LLM inference KV cache observability distributed systems MCP

inference optimization · model serving · KV cache · continuous batching · latency optimization

A from-scratch LLM inference engine for Apple Silicon: hand-written autoregressive decode loop, block-hashed KV-cache prefix reuse, continuous batching, and an OpenAI-compatible SSE streaming server. Not a wrapper around mlx_lm.generate — the scheduler, cache ownership, and admission path are all readable in one sitting.

67.7% lower median TTFT on a reusable 576-token prefix (240.50 → 77.76 ms), verified token-identical across 5 paired runs · 80.76 ms p95 TTFT at concurrency 8 (24 requests, one model forward per step) · 178.79 tok/s p50 vs 173.50 for mlx_lm.generate in an alternating-order paired baseline. M4, Qwen2.5-0.5B-Instruct-4bit, raw per-request JSON committed.

distributed systems · observability · static analysis · Go

Detects goroutine leaks and deadlocks from Go execution traces — no instrumentation, no code changes. Reconstructs goroutine provenance and runs leak, lock-cycle, channel-lock, orphan, and static-release analyses over the blocked runtime state that ordinary tests never reach.

94% on GoBench GoKer — 64/68 real production bugs · 0 false positives on a 224-goroutine net/http/httptest control.

context engineering · agent memory · knowledge graph · provenance · MCP

Local-first context graph for AI agents. Most memory layers help an agent remember; Palimp helps it remember safely. Every fact is namespace-scoped, source-linked with provenance, temporally versioned (valid_from / valid_until / as_of), and returned as data — never as hidden instruction (treat_as_instruction: false).

2–3 hop graph recall with depth decay · token-budgeted retrieval · contradiction tracking · 8 MCP tools · one inspectable SQLite file, zero external dependencies.

evals · LLM evaluation · cost optimization · observability · deterministic replay

An evaluation harness for model-routing decisions. A router reports gross savings from picking a cheaper model; that number says nothing about whether the request survived invalid tool calls, retries, failovers, or loop escalations. Ledger mines those failure signals from redacted telemetry, HMAC-pseudonymises identities, records Git/CI provenance, and deterministically replays content-free routing envelopes.

45/45 tests across privacy validation, telemetry mining, provenance, replay, and CLI · stores no prompts · reports recovery rows as correlated candidates, never as proven causation.

RAG · hybrid search · embeddings · reranking · retrieval · MCP

Local-first RAG pipeline for agent context: dense embeddings plus BM25, fused with reciprocal-rank fusion, optional FlashRank cross-encoder reranking, HyDE query expansion, and incremental hash-based reindexing. Explicit data roots — it never scans your home directory, and the corpus never leaves your machine.

6 MCP tools for collection and cross-collection search · every result returns a cited local source path and timestamp · two independent ranking signals fused before reranking.

LLM guardrails · evals · uncertainty quantification · calibration · logprobs

Single-pass uncertainty firewall for LLM output. Computes per-token Shannon entropy from provider logprobs, scores it against a calibrated reference distribution (CES, Villani et al. 2026), and returns a risk level a policy engine routes to allow / warn / block. Uses probabilities the provider already emits — no second judge-model call.

1 generation pass, not 2 · calibrated thresholds kept separate from raw scores · CLI, MCP, and RAG-guardrail surfaces. A risk signal, not a truth oracle — confident falsehoods still score low.

agent-board — agent orchestration multi-agent systems. A multi-agent control loop where an executor can never commit its own work: a planner writes tasks to a board, executors claim and build, and an orchestrator reviews every result. TODO → IN_PROGRESS → IN_REVIEW → DONE, failed reviews re-queued with a concrete fix note, bounded retries before ABANDONED. ~150 lines, standard library only.

agent-atlas-trust-audit — evaluation provenance audit trail. Deterministic claim-to-evidence audit engine: extracts every claim from a document, traces it to local source packets, hard-stops on contradictions instead of averaging them away, and exports a replayable audit bundle with SHA-256 fingerprints. 8/8 tests.

NexusCache — distributed systems caching gRPC observability. Distributed Go cache: consistent hashing, etcd discovery, singleflight stampede control, Prometheus metrics. 23.4K ops/s at 713 µs p50, 3-node cluster.

TitanQueue — distributed systems Redis. Fault-tolerant task queue: at-least-once delivery, lease-expiry recovery, priorities, retries, uniqueness, graceful shutdown.

unslop — prompt engineering agent skills. An agent skill that strips AI tells from prose without inventing facts to replace vague ones.

still-mini-kv-compactor — KV cache long context inference optimization. Trained a 1.3M-param Perceiver KV compactor against a frozen Qwen2.5-0.5B teacher. At 8× compression it landed at 0.12 retrieval accuracy vs 0.54 for plain sink+recent. The write-up isolates why: compacting in rotated (RoPE) key space destroys the positional geometry queries retrieve against.

dag-moe-reproduction — mixture of experts reproducibility. DAG-MoE (ICML 2026) on an M4. Mechanism verified; 7.64 val loss vs 7.59 matched baseline — a null result, and an argument for why laptop scale can't test the paper's actual claim.