---
title: 'Lead QA Engineer at Titan AI'
canonical: 'https://feeny.ai/job/lead-qa-engineer-titan-ai-united-states-wvejvm0gv5z3'
type: 'job'
last_seen: '2026-09-10'
---

# Lead QA Engineer at Titan AI

- **Company:** Titan AI
- **Location:** United States
- **Compensation:** $140k–$165k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-05-27
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/titan-ai/c9112496-3376-4d02-a222-d6ff9f424dd1

## Job description

## About Titan

Titan builds AI software for banks: purpose-built small language models, a banking ontology, and AI bankers that financial institutions can trust. Our models outperform general-purpose LLMs by 30 to 80 percent on banking tasks. Customers include community banks, credit unions, and large regional and super-regional institutions. We are backed by leading fintech investors and operate under the compliance, audit, and model-risk standards that banking requires.

## Why This Role Exists

Titan is scaling from a handful of live banking customers to thirty, then to hundreds. Right now, there is no formal QA function. There is no evaluation framework, no regression baseline, no quality gate in CI/CD. A QA failure at a bank is not a user experience problem. It is an operational and regulatory risk. This role exists because that gap has to close before the customer count grows.

This is a hands-on, individual-contributor role first. You are coming in to do the work: write the test cases, build the evaluation framework, set up CI/CD gates, and triage bugs alongside engineering. The function gets built because you build it yourself. Once the practice is stable and documented, you bring in QE engineers to scale it.

## What You Own

AI Evaluation. You personally design and execute the evaluation framework for LLM and agentic AI outputs across Foundry, Agent Builder, and client-deployed instances. You write the assertions, define the behavioral contracts, and own regression baselines for model behavior. Standard QA methods break down here: you cannot write a deterministic assertion for whether an AI accurately summarized a 200-page loan agreement. You need to think in distributions and confidence intervals, and you need to build tooling that does too.

Test Coverage. You write and maintain the automated test suite: end-to-end, integration, and regression coverage for backend APIs, document ingestion pipelines, AI inference workflows, and frontend surfaces. You own performance and load testing for latency-sensitive inference paths. You set up and enforce quality gates in CI/CD pipelines. When a bug surfaces in production, you are in the triage, you write the reproduction case, and you own the regression test that prevents it from coming back.

Compliance and Client Quality. You produce the test artifacts, audit logs, and process documentation that meet SOC 2 Type II standards. You work directly with Forward Deployed Engineering on client-side validation and production issue reproduction. Bank examiners will scrutinize this work. It needs to be defensible on its own.

## Who You Are

Seven or more years in software QA engineering, with at least two years personally testing AI or ML systems. You have written test cases against LLM outputs, built evaluation pipelines from scratch, and know the difference between a flaky test and a genuinely non-deterministic system. You are fluent in Python and have built automated suites using pytest, Playwright, or Selenium. You have hands-on experience with RAGAS, DeepEval, LangSmith, or comparable evaluation tooling—not just familiarity with the names.

You can trace a failure from the application layer to infrastructure and know enough about Azure, async systems, and REST APIs to do it without waiting on an engineer to walk you through it. You have integrated QA gates into CI/CD pipelines and owned the process end to end. Experience in fintech, banking, or another regulated environment is a strong advantage. Familiarity with document processing pipelines, multi-agent architectures, RAG validation, or observability tooling such as Arize or Langfuse puts you ahead. You are not here to manage. You are here to build and test.

What Success Looks Like In your first 90 days: a diagnostic of current test coverage shared with engineering leadership, an evaluation framework running against at least one AI-powered workflow that you built yourself, and quality gates live in CI/CD. In your first six months: regression baselines established for model behavior, SOC 2 test artifacts documented and audit-ready, and the test suite running on every release without manual intervention. At one year: the function is staffed, coverage scales with every product release, and quality is a first-class input to every deployment decision. The work you did personally is the foundation the team builds on.

## Compensation and Structure

- Competitive base and meaningful equity.
- Remote (US). Occasional travel to client sites and team offsites.

## About Titan AI

## Company Overview
- **One-liner**: Titan builds a secured, auditable AI platform with proprietary banking-native models and agents purpose-built for financial services.
- **Entity Type**: Private (early-stage startup, no disclosed funding round)
- **Headquarters**: New York, NY, United States (with additional office in Greenwich, CT)
- **Founded**: 2025
- **Founders**: Arjun Sirrah (Founder & CEO)

## Core Business
- Primary industry: AI platform for banking / Financial Technology (FinTech)
- Target customers: Banks and financial institutions (B2B, Enterprise, highly regulated)
- Mission statement: Build AI that thinks like a bank – delivering explainable, auditable, and secure AI for banking operations, compliance, and credit.

## Products & Services
- **Titan Banking Platform**: A unified interface providing access to proprietary banking-small language models (SLMs), general-purpose LLMs, and built-in Banking Agents. Handles tasks like consumer credit processing, regulatory compliance, bank operations, and product inquiries. Key features include auditable chain-of-thought, human-in-the-loop workflows, role-based access control, and SOC 2 compliance. (Type: SaaS / AI Platform)
- **Titan Banking Agents**: Agentic workflows that leverage Titan’s banking models to automate reviews, exception processing, and deep research – designed to mirror how top-performing bankers reason. (Type: AI Agent / Workflow Automation)

## Market Standing
- **Valuation/Market Cap**: Not publicly available
- **Key Metric**: Headcount of 14 employees (as of mid-2026) with 23.8% monthly growth; 12 active job openings.
- **Notable Investors/Partners**: Board includes **Blake Paulson** (former Acting Comptroller of the Currency). The company’s team includes former senior bank operators, OCC regulators, and AI engineers from KeyBank, Laurel Road, and other fintechs.
- **Growth Signals**: Rapid hiring (+71.4% job postings quarterly), early preference results showing 80% banking task preference over ChatGPT and 97% over Gemini 2.5 Pro, and strong domain credibility with a founding team that scaled Laurel Road to $1B+ annual originations.

## Competitive Advantages
- **Banking-native ontology**: Models are not fine-tuned general LLMs – they embed proprietary banking logic, regulatory knowledge, and credit risk reasoning from the ground up.
- **Auditability & explainability**: Full logging of prompts, chain-of-thought, and decisions – designed for examiner review and regulatory defense.
- **Security & compliance**: SOC 2 compliant, AES-256 encryption, RBAC, MFA, and a dedicated data protection layer that never uses customer data to train public models.
- **Human-in-the-loop**: AI recommends, bankers approve – preserving domain expertise and accountability.

## Strategic Focus
- Scaling enterprise sales to mid-size and large banks (evidenced by hiring Account Executives and Client Partners).
- Expanding the proprietary model’s performance lead through continuous training on banking-specific data and the Banker Trust Index evaluation.
- Growing the team across engineering (Forward Deployed, AI, QA) and go-to-market roles.

## Why Work Here
- **Culture**: Small, fast-moving startup (14 people) with deep banking expertise – you’ll work directly with former regulators, bank CTOs, and fintech veterans.
- **Engineering culture**: Emphasis on applied AI, full-stack development, and forward deployment inside customer environments. High ownership and impact – engineers build features that directly affect how banks operate.
- **Remote/Hybrid**: Not explicitly stated; HQ in New York with a Greenwich, CT office – likely in-office or hybrid for many roles.
- **Notable perks**: Opportunity to shape AI for a heavily regulated industry, work on explainable/auditable models, and learn from a team that includes a former Acting Comptroller of the Currency.

## Sources
1. [titanbanking.ai](https://www.titanbanking.ai/)
2. [titanbanking.ai/about-company](https://www.titanbanking.ai/about-company)
3. [linkedin.com/company/titan-os](https://www.linkedin.com/company/titan-os)
4. [jobs.ashbyhq.com/titan-ai](https://jobs.ashbyhq.com/titan-ai)
5. [titanbanking.ai/why-titan](https://www.titanbanking.ai/why-titan)

## Other roles at Titan AI

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