---
title: 'Applied AI Engineer at Titan AI'
canonical: 'https://feeny.ai/job/applied-ai-engineer-titan-ai-united-states-p8324tr5drnp'
type: 'job'
last_seen: '2026-09-10'
---

# Applied AI Engineer at Titan AI

- **Company:** Titan AI
- **Location:** United States
- **Compensation:** $200k–$300k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-05-28
- **Last confirmed live:** 2026-09-10
- **Apply:** https://jobs.ashbyhq.com/titan-ai/297cf9a9-289d-4cd5-a4a1-1e051f6f5d64

## 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. We operate under the compliance, audit, and model-risk standards that banking requires.

## Why This Role Exists

Titan is growing from a handful of live banking customers to thirty, then to hundreds. This role sits across the AI Toolbelt and Product Engineering lanes, owning the production AI systems that bank employees use every day — agent workflows, retrieval pipelines, and LLM integration layers. We bring a problem and expect a working solution.

## What You Own

- Agent orchestration frameworks for multi-step reasoning, tool use, and constraint-based problem solving across banking workflows
- RAG pipelines covering embedding generation, chunking, hybrid retrieval, and retrieval evaluation, calibrated for banking document types
- LLM integration layers connecting banking models, APIs, and knowledge bases into reliable, auditable inference workflows
- Evaluation infrastructure including behavioral contracts, regression baselines, and production observability for non-deterministic AI outputs
- Backend services and APIs powering client-facing AI products at bank-tier uptime requirements

## Who You Are

Background in software engineering with at least five years of experience, the last two spent building and operating production AI systems. Shipped agentic workflows, RAG pipelines, or LLM-powered applications to real users. Strong Python fundamentals across APIs and async systems, which is the foundation the AI work sits on. Comfortable picking the practical solution over the clever one. Fluent in LangChain, LangGraph, PydanticAI, or AutoGen, with hands-on experience with vector databases, retrieval evaluation, and observability tooling such as LangSmith, RAGAS, Arize, or Langfuse. Prior fintech or banking experience is a genuine advantage, not a checkbox.

Required Qualifications

- 5+ years software engineering; 2+ years building and shipping production agentic AI or RAG systems
- Agent framework experience: LangChain, LangGraph, PydanticAI, AutoGen, or Semantic Kernel
- RAG stack proficiency: embedding models, vector DBs (Pinecone, Weaviate, Milvus, FAISS), hybrid search, retrieval evaluation
- LLM integration depth: tool calling, structured outputs, multi-step reasoning, behavioral regression testing
- AI eval and observability tooling: LangSmith, RAGAS, DeepEval, Arize, Langfuse, or equivalent
- REST APIs, async Python, microservices; Azure cloud experience preferred

Strongly Preferred

- Fintech, banking, or regulated industry experience
- Graph databases (Neo4j, ArangoDB, Dgraph) and MCP / connector architecture
- Multi-agent or planner-based AI architectures
- Multi-tenant SaaS with auditability and compliance requirements

What Success Looks Like Within 90 days, ownership of at least one production AI workflow end to end with measurable improvements shipped to the retrieval or agent layer. Within six months, the go-to person on the team for hard agent and retrieval problems, operating independently from a high-level brief through to recommendation and implementation. At one year, a senior anchor on the AI engineering function with a track record of pulling others up and a credible path to leading other AI Engineers.

## 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

- [Principal Forward Deployed Engineer – Applied AI Focus](https://feeny.ai/job/principal-forward-deployed-engineer-applied-ai-focus-titan-ai-united-states-nsjm6vhbvz5x) — United States
- [Forward Deployed Engineer – Applied AI Focus](https://feeny.ai/job/forward-deployed-engineer-applied-ai-focus-titan-ai-united-states-c4qbvw2dmdeb) — United States
- [Forward Deployed Engineer – Product Focus](https://feeny.ai/job/forward-deployed-engineer-product-focus-titan-ai-united-states-332mrmvkk44g) — United States
- [VP of Site Reliability](https://feeny.ai/job/vp-of-site-reliability-titan-ai-united-states-tahcxfbwz56m) — United States
- [QA Engineer](https://feeny.ai/job/qa-engineer-titan-ai-united-states-zqxcc90qxaa4) — United States
- [Lead QA Engineer](https://feeny.ai/job/lead-qa-engineer-titan-ai-united-states-wvejvm0gv5z3) — United States
- [AI Platform Analyst](https://feeny.ai/job/ai-platform-analyst-titan-ai-united-states-jt9kxwf85kd7) — United States
- [Client Partner](https://feeny.ai/job/client-partner-titan-ai-united-states-mvzhhbxcvtw5) — United States
- [Senior Solutions Engineer](https://feeny.ai/job/senior-solutions-engineer-titan-ai-united-states-jkykfr5vzagq) — United States
- [Account Executive, Enterprise](https://feeny.ai/job/account-executive-enterprise-titan-ai-united-states-axx9hcac2tdh) — United States