Open to opportunities

Artificial Intelligence Engineer at Ikka Labs.

Building an internal cloud platform and an MCP-based infrastructure assistant. LLMs, RAG pipelines, and agentic systems. M.S. Machine Learning, Stevens.

Portrait of Rishi Chhabra

Now.

Ikka Labs · New York

At Ikka Labs I'm engineering a self-service platform that provisions caching engines (Valkey, Redis, Dragonfly, Memcached) and streaming brokers (Kafka, Redpanda) across seven deployment targets (on-prem Kubernetes and managed/self-managed AWS and Azure) behind OIDC/OAuth2 SSO and HashiCorp Vault.

The interesting part is the MCP server: an AI assistant that provisions and diagnoses infrastructure in plain language under user-level RBAC, paired with a read-only diagnostic agent (k8sgpt) that flags anomalies and recommends fixes. The goal isn't a chatbot; it's making a complex platform legible to the people who have to operate it.

Selected work.

04 case studies
01LLM Infra · Cost

A cost-aware gateway that routes LLM traffic by how hard the question is

−68%
inference cost
$0.48 → $0.15
per batch
93.4%
routing accuracy
0.06 ms
to decide

Most LLM spend is wasted sending easy prompts to an expensive model. This gateway puts a cheap complexity classifier in front of the models (no inference, ~0.06 ms per decision) and keeps 79% of traffic on a self-hosted Llama 3.2 via vLLM, escalating only the genuinely hard 21% to GPT-4o.

The tradeoff worth naming: a heuristic classifier is fast and free but occasionally under-routes, so the escalation path and a per-query cost dashboard exist to catch that. On an 81-query benchmark it held 93.4% accuracy while cutting cloud inference spend by roughly two-thirds, the difference between a demo you can afford and one you can't.

query prompt in complexity classifier ~0.06 ms · no inference Llama 3.2 · vLLM self-hosted · cheap GPT-4o escalate hard cases 79% 21%
Fig. 1 · one cheap decision decides which model pays for the answer.
Stack · FastAPI · vLLM · Llama 3.2 · GPT-4o · Streamlit · Docker Source ↗
02RAG · Production

Production GraphRAG over financial documents, with eval gates in CI

+40%
retrieval accuracy
−30%
hallucinations
−40%
debug time

At AriesView I built a retrieval system over dense financial filings: Weaviate hybrid search for recall, Neo4j GraphRAG for the relationships plain vector search misses, and multi-agent workflows in LangGraph and MCP on top.

The part that made it production rather than a notebook: RAGAS and DeepEval wired as CI/CD eval gates. A prompt or model change that quietly degrades faithfulness fails the build instead of shipping. That single decision is what moved retrieval accuracy +40%, cut hallucinations 30%, and took 40% off debugging. You find the regression in review, not from a user.

Stack · Weaviate · Neo4j · LangGraph · MCP · RAGAS · DeepEval · GitHub Actions AriesView · 2025–26
03Evaluation · Observability

An automated evaluation pipeline that treats RAG quality as a build check

95%
quality gate on merges
3 metrics
per pull request
−40%
debug time

On every pull request this pipeline runs the chain through three metrics (Faithfulness, Answer Relevancy, Contextual Precision) scored with a small judge model, and blocks the merge if any drops below 95%. LangSmith traces each retrieval and generation with latency and token counts, and A/B comparisons make prompt changes measurable instead of vibes.

The point is boring on purpose: evaluation belongs in the same place as the rest of the tests. Once quality is a gate, regressions stop being a production incident and become a red check in code review.

Stack · DeepEval · RAGAS · LangSmith · GitHub Actions · Claude Haiku Source ↗
04Platform · Media

A self-hosted video platform with near-real-time captions on CPU

~30×
real-time captions
CPU-only
Whisper
24
Kubernetes objects

Also at Ikka Labs: a self-hosted video platform on PeerTube, PostgreSQL, Redis, and S3, with two Python ingestion workers generating Whisper captions at ~30× real-time on CPU (no GPU budget required), deployed on OpenShift via Kustomize (24 Kubernetes objects).

On top sits a summarization and Q&A assistant running a local Llama 3.1 8B via Ollama, with semantic search over transcripts (nomic-embed-text), topic classification, and prompt-injection guardrails against adversarial queries. Private by construction, since nothing leaves the cluster.

Stack · PeerTube · Whisper · Ollama · Llama 3.1 8B · nomic-embed · OpenShift · Kustomize Ikka Labs · 2026

Writing.

Medium ↗

I write about the reality of building ML systems, the parts that don't show up in a benchmark: what retrieval actually costs, why evaluation belongs in CI, and where agent architectures earn their complexity. Notes and case studies, in the spirit of the engineers I learn from.

Routing LLM traffic by complexity, not by default

The classifier-in-front pattern, and the cost math behind a 68% reduction.

Eval gates: making RAG quality a build check

Wiring Faithfulness and Answer Relevancy into CI so regressions fail loudly.

When GraphRAG beats plain vector search

Relationships a flat index can't represent, and what Neo4j buys you.

Experience.

8 roles
Jun 2026
Present
Ikka Labs
New York, NY
Artificial Intelligence Engineer

Building a self-service platform that provisions caching engines (Valkey, Redis, Dragonfly, Memcached) and streaming brokers (Kafka, Redpanda) across 7 deployment targets (on-prem Kubernetes, AWS, Azure), secured with OIDC/OAuth2 SSO (Dex) and HashiCorp Vault. Shipped an MCP server powering a natural-language infrastructure assistant under user-level RBAC, with a read-only k8sgpt agent that flags anomalies and recommends fixes.

Sep 2025
May 2026
AriesView
Boston, MA (Remote)
Artificial Intelligence Engineer

Production RAG over financial documents: Weaviate hybrid search + Neo4j GraphRAG with RAGAS/DeepEval eval gates and LangGraph/MCP workflows. +40% retrieval accuracy, −30% hallucinations.

Feb 2024
Aug 2024
Incuwise
Noida, India
Software Development Engineer I

Migrated legacy infrastructure to serverless AWS (Lambda, API Gateway) and built high-throughput Node.js backend services. +60% scalability, 99.9% uptime, 4 apps for 10,000+ users, −15% API response time.

Earlier
Lead Application DeveloperGoogle Developer Student Clubs · CUH2023–24
Full Stack Flutter DeveloperYASH Infonet · Noida2023
Flutter Developer InternUnicel Corporation · New Delhi2022–23
Flutter Developer Interndappunk · Dubai (Remote)2023
PR & Outreach HeadGeeksForGeeks Student Chapter · CUH2022–23

About.

New York Metro Area

Rishi Chhabra

I have a computer-science background and an M.S. in Machine Learning from Stevens Institute of Technology (May 2026). My work lives between the model and the product (retrieval, agent tooling, evaluation, and the cloud infrastructure underneath), and I care as much about latency, cost, and failure modes as about accuracy.

Before the LLM work I shipped serverless backends to real users, which is where the instinct for operability comes from. I'm looking for teams putting ML into production and willing to sweat the systems around it.

Claude Certified Architect Databricks GenAI Engineer AWS ML Engineer (Associate) AWS Developer (Associate) Databricks Spark Developer