---
title: 'Senior AI Engineer — Inference & Agent Systems at Arcana Analytics'
canonical: 'https://feeny.ai/job/senior-ai-engineer-inference-agent-systems-arcana-analytics-united-states-q4ws5endktvs'
type: 'job'
last_seen: '2026-09-04'
---

# Senior AI Engineer — Inference & Agent Systems at Arcana Analytics

- **Company:** Arcana Analytics
- **Location:** United States
- **Posted:** 2026-03-15
- **Last confirmed live:** 2026-09-04
- **Apply:** https://job-boards.greenhouse.io/arcanaanalytics/jobs/4183986009

## Job description

Title: Applied AI Engineer — Inference & Agent Systems

Location: United States

## What We're Building

Arcana is building AI agents that synthesize information across heterogeneous sources and deliver structured, reasoned answers in real time. The product only works if the agents are fast, reliable, and correct, not approximately correct.

Our stack: Go + Temporal for orchestration, a Plan-Execute-Synthesize agent architecture, and an evaluation harness we use to measure every regression. The problems are hard. The latency bar is aggressive. The accuracy requirements are unforgiving.

The Work

Inference Optimization

- Drive TTFT below 400ms for multi-step agent pipelines
- Streaming optimization: first token to user while sub-agents are still running
- KV cache strategy, prompt compression, dynamic context window management
- Multi-provider routing: model selection by latency, cost, and task type across OpenAI, Anthropic, Gemini, and open-weight models

Agent Architecture

- Design and implement Plan-Execute-Synthesize pipelines that run sub-agents in parallel DAGs, not sequential chains
- Build reliable orchestration on top of Temporal: retries, timeouts, partial failure recovery, idempotency
- Structured output enforcement: JSON schema validation, retry loops on malformed LLM output, graceful degradation
- Tool call design: schema design that LLMs actually follow reliably across providers

Evaluation & Harness

- Own the eval framework end to end: ground truth datasets, automated scoring pipelines, regression detection on every PR
- LLM-as-judge pipelines for qualitative output assessment
- Latency regression testing - p50/p95/p99 tracked across every deployment
- Adversarial test case design: ambiguous queries, missing data, conflicting sources, malformed tool responses

Infrastructure

- Model serving and cold start optimization
- Async worker architecture for parallel sub-agent execution
- Observability: trace every token, every tool call, every synthesis step

## What We're Looking For

You've built something that runs in production at a meaningful scale and you understand why it's fast (or why it isn't).

Strong signal:

- You've worked on inference pipelines where TTFT was the primary metric and you moved it meaningfully
- You've built multi-step agent systems and you know where they break not from reading papers but from watching them fail in production
- You've written eval harnesses from scratch and you have opinions about what makes a ground truth dataset actually useful
- You've debugged LLM non-determinism in production and built systems resilient to it
- You've worked with streaming LLM responses and built infrastructure around partial output handling

Weaker signal (but not disqualifying):

- You've fine-tuned models but haven't shipped inference systems
- You've used LangChain/LlamaIndex but haven't built the layer underneath
- Strong ML research background without systems exposure

Stack familiarity (we care more about depth than match): Go, Python, Temporal, Kafka, PostgreSQL, Docker

## Why This Role

The problems here don't have blog posts about them yet. Parallel agent DAG execution under hard latency budgets, streaming synthesis across partial sub-agent results, eval harnesses for non-deterministic multi-step systems: these are genuinely unsolved at production quality. Small team. High ownership. Every engineer's decisions ship to production.

Who We Want to Hear From

You've shipped inference systems at:

- A real-time AI product (search, coding assistant, chat at scale)
- A model serving infrastructure company
- An agent platform (any domain)

Or you've built eval/harness infrastructure that a team of 10+ engineers actually trusted to catch regressions.

Apply

Send to: [careers@arcana.io]

Include:

- One system you built where latency was the primary constraint what you measured, what you changed, what moved
- Link to anything public (code, writing, talks)
- No cover letter required

We respond to every application.

## About Arcana Analytics

## Company Overview
- **One-liner**: Arcana provides a portfolio intelligence platform that enables institutional investors to analyze risk, decompose performance, and surface alpha through proprietary crowding, ownership, and factor datasets.
- **Entity Type**: Private (Series A)
- **Headquarters**: New York, New York, United States
- **Founded**: 2022
- **Founders**: Rich Falk-Wallace (CEO) and Siva Visakan Sooriyan (CTO)

## Core Business
- Primary industry/industries: Financial technology (FinTech), investment analytics, risk management
- Target customers: Institutional investors – hedge funds, asset managers, and portfolio managers (B2B, Enterprise)
- Mission or purpose statement: “Portfolio intelligence for hedge funds and asset managers — analyze risk, decompose performance, and surface alpha.”

## Products & Services
- **Risk Model Core**: Sophisticated fundamental equity factor risk models with parsimonious core factors and extensive custom factor library. Provides long- and short-horizon analysis and visibility. (SaaS)
- **Ownership, Crowding, & Performance Database**: Live short interest data, treasury holdings, and full idio/factor model for precise crowding and positioning insights. (Data feed / SaaS)
- **Optimization, Scenarios, Screening, & API**: Tools for building analytically‑constructed books, running scenario analysis, idea/hedge screening, and advanced API access. (SaaS + API)
- **Cross‑Market Synthesis**: Combines single‑stock options, implied earnings moves, macro exposures, consensus estimate revisions, and positioning to explain stock movement drivers. (SaaS)

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Total funding raised – $5.3M (CBInsights); Series A led by D1 Capital Partners and Abstract Ventures (LinkedIn notes additional investors including Duquesne (Stan Druckenmiller), Tiger Global, GoldenTree Asset Management, Ryan Roslansky, and Akshay Kothari)
- **Notable Investors/Partners**: D1 Capital, Duquesne, Tiger Global, Abstract Ventures, GoldenTree Asset Management, Ryan Roslansky (CEO LinkedIn), Akshay Kothari (COO Notion)
- **Growth Signals**: Headcount grew ~180% YoY to 194 employees. Active job postings increased +166.7% year over year (24 open roles). LinkedIn followers surged +3846.9% YoY. Expanding presence in India (Bangalore, Coimbatore) and Ecuador.

## Competitive Advantages
- Only provider combining live short‑interest data with full factor risk and idiosyncratic models for unprecedented crowding accuracy.
- Sophisticated factor models explain ~75% of large‑cap single‑stock moves, giving clients clarity on drivers vs. residual alpha.
- 20‑year factor model history and live intraday factor returns produce real‑time portfolio insights.
- Institutional backing and advisory from top‐tier investment firms and tech leaders.

## Strategic Focus
- Building AI agents that synthesize information across heterogeneous sources to deliver structured, actionable insights (multiple Senior AI Engineer roles posted).
- Expanding client coverage globally (hiring Accounts Director for Hong Kong/APAC) and deepening engineering talent in India.
- Scaling the platform to serve a broader set of institutional clients with optimization, scenario analysis, and API workflows.

## Why Work Here
- Rapidly growing startup (180% YoY headcount growth) with proven product‑market fit and prestigious investors.
- Hybrid/on‑site culture: roles in New York, Bangalore, and Coimbatore; about 46% of openings are hybrid, 50% on‑site, 4% remote.
- Exposure to cutting‑edge work in financial risk modeling, AI, and data engineering.
- Notable perks: working with proprietary datasets, direct impact on a product used by top hedge funds, and a collaborative “analytically driven” problem‑solving environment.
- Engineering culture emphasizes modern infrastructure, AI agents, and real‑time data pipelines.

## Sources
1. [arcana.io](https://www.arcana.io/)
2. [LinkedIn – Arcana Analytics](https://www.linkedin.com/company/arcanaanalytics)
3. [CBInsights – Arcana](https://www.cbinsights.com/company/arcana-1)
4. [Greenhouse Careers – Arcana Analytics](http://job-boards.greenhouse.io/arcanaanalytics)
5. [Jobera – Arcanaanalytics Careers](https://jobera.com/employer/arcanaanalytics/)

## Other roles at Arcana Analytics

- [Sales Director](https://feeny.ai/job/sales-director-arcana-analytics-hong-kong-3mq46kwt2bfd) — Hong Kong, China
- [Sales Development Representative](https://feeny.ai/job/sales-development-representative-arcana-analytics-new-york-041jtzkwv8nm) — New York, NY
- [Vice President, Analysts & Client Support](https://feeny.ai/job/vice-president-analysts-client-support-arcana-analytics-bengaluru-1ptk6z28eed7) — Bengaluru, India
- [Client Solutions Associate](https://feeny.ai/job/client-solutions-associate-arcana-analytics-new-york-cjabx1zp01mg) — New York, NY
- [Portfolio Analytics Specialist](https://feeny.ai/job/portfolio-analytics-specialist-arcana-analytics-new-york-v3md429mwjq8) — New York, NY
- [Office Manager](https://feeny.ai/job/office-manager-arcana-analytics-bengaluru-8fr8hyfc6129) — Bengaluru, India
- [Staff Frontend Engineer](https://feeny.ai/job/staff-frontend-engineer-arcana-analytics-bengaluru-pxn8scz249p5) — Bengaluru, India
- [Customer Success Manager](https://feeny.ai/job/customer-success-manager-arcana-analytics-india-5pfjws22fjrz) — India
- [Senior AI Engineer — Inference & Agent Systems](https://feeny.ai/job/senior-ai-engineer-inference-agent-systems-arcana-analytics-bengaluru-a2gtfsq4gpva) — Bengaluru, India
- [Staff Software Engineer (Data Platform)](https://feeny.ai/job/staff-software-engineer-data-platform-arcana-analytics-bengaluru-d58zrzt9hx34) — Bengaluru, India