I architect, build, and own the AI systems that run inside large companies — RAG platforms, evaluation infrastructure, model-routing pipelines, and the deployment plumbing that keeps them up at 99.9%.
Headline numbers
85K
requests / day
6,000
users served
99.9%
uptime
−30%
inference cost
+0.12
RAGAS faithfulness
Experience
Where I've been working
AI Architect · CGI
Dec 2024 — Present · Remote
Where I am now. The work splits roughly into three buckets: building the platforms that host AI systems, making sure those systems give correct answers, and keeping the costs sane.
—HR Assistant · 6,000 employees
Sole architect on an end-to-end Azure RAG system for an HR Policy chatbot. Hybrid retrieval (vector + BM25 + semantic reranker), GPT-4o on PTU, Document Intelligence, Entra ID/MFA, private VNet. <3s response, ~40% cache hit rate, 99.9% uptime, EU AI Act-aligned.
—Conversational AI Platform · 85K req/day
Architected and own a Kubernetes-based platform on AWS EKS serving ~85K requests/day across 10+ agent workflows for 1,200 users. Lead a 5-person team with full SLA accountability.
—Inference cost · −30%
Co-designed a complexity-based model router sending simple queries to Gemma/Mistral 8B and complex ones to GPT-4o. Cut monthly inference spend from $15K to $10.5K with no quality degradation.
—Hallucination · 45% → 30%
Fine-tuned Llama 3 via LoRA/PEFT with DPO on 500+ human-labeled preference pairs. Reduced measured hallucination rate from 45% to 30% across production workloads.
—EvalOps · regression gate in CI
Built a full EvalOps pipeline — groundedness rubrics, golden datasets, automated RAGAS scoring, OpenTelemetry retrieval tracing — as a mandatory CI gate before every model update. No quality regressions have reached production undetected since rollout.
—AI Vehicle Inspection
Lead engineer on an AI-powered vehicle inspection system for an enterprise logistics client, replacing manual walk-around inspections. Drove the strategic pivot from a YOLOv8 model to GPT-4o Vision for zero-shot damage understanding.
Two years building the foundations: training pipelines, retrieval systems, evaluation tools — the infrastructure my later architecture work would rely on.
—Retrieval faithfulness · 0.62 → 0.74
Tuned a hybrid retrieval system (BM25 + vector + cross-encoder reranking) with query rewriting. Lifted RAGAS faithfulness from 0.62 to 0.74 on a 500-question eval set — a +0.12 absolute jump on a hard-to-move metric.
—20% of bad updates blocked
Built a CI/CD pipeline using LLM-as-judge with rubric scoring as a mandatory release gate. Caught about a fifth of regressive model updates before they reached users.
—ML pipeline cycle · 72h → 24h
Rebuilt Kubernetes ML training pipelines for a 6TB document classification workload. Cut the train-to-deploy cycle from three days to under one — a 3× faster iteration loop.
—Observability · hours to minutes MTTD
Implemented CloudWatch and OpenTelemetry instrumentation with drift detection and alerting. Reduced mean time to diagnose failures from hours to minutes.
My first proper engineering job — at a healthcare startup. Mostly streaming data pipelines, dead-letter queues, and learning what production really means.
—+40% pipeline throughput
Tuned partitioning and parallelism on the Kafka and Spark setup. Added schema validation and dead-letter handling so a single bad record couldn't take down the whole pipeline.
—−15% application crashes
Tracked down recurring failure patterns and rebuilt the logging, monitoring, and alerting.
Apache KafkaApache SparkPythonAWS
Live demo
What I actually do, made visible
A lot of my work is invisible — it's the part of an AI system that happens between you typing a question and the answer appearing. Below is a small interactive version, using my own work history as the “knowledge.”
Type a question (or pick a preset), and you'll see the steps the system goes through: breaking down the question, searching for information, ranking what it found, checking the answer is trustworthy, and writing a response.
presets:
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Side projects
Things I built on my own time
Side projects where I get to try out ideas I've been thinking about. All open source.
Senior or lead AI engineering roles at teams that are serious about reliability, cost, and measurable quality — not just shipping demos. Especially interested in companies building AI products where the stakes are real: enterprise tools, healthcare, finance, infrastructure.
The fastest way to reach me is email. I read everything — so if you're building something interesting, send a note.