Auquan Ltd

AI-driven financial insights from unstructured data

AI Applied Engineer

AI Application EngineeringConfirmed live in the last 24 hours

Full-timeNew York, NY, USA

About the Job

Requirements

At least 4 years of software development experience in fast-paced, product-focused environments.
Experience owning a subsystem or workflow end-to-end, from requirements through architecture, implementation, and launch.
Strong Python fundamentals and the ability to think in primitives, patterns, and systems.
Current use of AI to automate work or build large language model-powered workflows.
Ability to translate customer ambiguity into clear technical solutions.
Ability to unblock yourself through learning, experimentation, and AI tools.
Comfort operating under tight deadlines, high expectations, and minimal process.
Familiarity with the technology stack: Python, React, Next.js, FastAPI, MongoDB, and Redis.

Responsibilities

Translate ambiguous workflow requests into scoped, architected, implemented, and deployed multi-agent systems.
Build reliable multi-step, multi-agent workflows used in production by financial institutions.
Own reliability, latency, success metrics, and customer adoption.
Work directly with customer engineering teams to deliver smooth deployments.
Convert bespoke workflows into reusable platform primitives and components.
Build orchestration logic, tools, templates, and pipelines used across engineering pods.
Ship first versions in days while operating with high agency, tight prioritisation, and strong defaults.
Identify patterns across customers and turn them into reusable systems.
Influence the architecture and direction of core platform components.
Use AI for coding, testing, debugging, workflow design, and documentation.
Build evaluations, safety nets, and AI-native engineering workflows.

Desired Qualifications

Experience building or orchestrating multi-step large language model or agentic workflows.
Experience with financial workflows such as credit, investment, ESG, and risk.
Infrastructure and operations exposure, including Kubernetes, Terraform, Azure or AWS, continuous integration/continuous delivery, and observability.
Experience at early-stage startups.
Experience building internal frameworks, tooling, or templates used by multiple teams.
Experience with evaluations, guardrails, or workflow orchestration systems.

About the company

ALFA develops autonomous AI agents that eliminate the repetitive, manual work slowing down financial institutions. Our technology supports credit, investment, ESG, and compliance workflows for leading firms including Goldman Sachs, UBS, CVC, and MetLife. Today, 40% of the world’s top 50 financial institutions use ALFA to remove manual processes, speed up decision-making, and achieve greater strategic impact.

With teams growing across the US, UK, and India, we’re building an elite force of innovators ready to redefine what’s possible with AI and machine learning.

About the role
This role sits at the intersection of systems architecture, agentic AI workflows, and customer-facing problem solving. You’ll design multi-agent systems, build reusable primitives, and convert complex financial workflows into scalable automated products.

You’ll work directly with leading investment firms and own the lifecycle of every agent you build—from design to reliability, deployment, and scale.

Team & Culture
High-agency, high-velocity, no-politics.
We move fast, unblock ourselves, work directly with customers, give clear feedback, and minimise process. We’re building the platform that automates analytical work across finance — a category-defining opportunity.
If you want to own meaningful outcomes, build high-leverage AI systems, and move faster than almost anywhere else, you’ll fit right in.

Responsibilities

Take ambiguous workflow requests and turn them into scoped, architected, implemented, and deployed multi-agent systems
Build reliable multi-step, multi-agent workflows used in production by financial institutions
Own reliability, latency, success metrics, and customer adoption
Work directly with customer engineering teams to deliver smooth deployments
Convert bespoke workflows into reusable platform primitives and components
Build orchestration logic, tools, templates, and pipelines used across engineering pods
Ship V1s in days, operate with high agency, tight prioritisation, and strong defaults
Identify patterns across customers and turn them into reusable systems
Influence architecture and direction of core platform components
Use AI for coding, testing, debugging, workflow design, and documentation
Build evals, safety nets, and AI-native engineering workflows

Requirements

Must-Haves

4+ years of software development in fast-paced, product-focused environments
Owned a subsystem or workflow end-to-end (requirements → architecture → implementation → launch)
Strong Python fundamentals; think in primitives, patterns, and systems
Already use AI to automate work or build LLM-powered workflows
Comfortable translating customer ambiguity into crisp technical solutions
Able to unblock yourself through learning, experimentation, and AI tools
Thrive under tight deadlines, high expectations, and minimal process
You'll have a solid grasp of our tech stack: Python, React / NextJS, Fast API, MongoDB, Redis

Nice-to-Haves

Built or orchestrated multi-step LLM or agentic workflows
Experience with financial workflows (credit, investment, ESG, risk)
Infra/ops exposure (Kubernetes, Terraform, Azure/AWS, CI/CD, observability)
Experience at early-stage startups
Built internal frameworks, tooling, or templates used by multiple teams
Experience with evals, guardrails, or workflow orchestration systems

Benefits

We are an office-first company and expect regular in-person collaboration as part of this role. You should also be comfortable collaborating across time zones (US, UK, India).
We offer a competitive package designed to support you both inside and outside of work:

Base Salary: $170,000 - $200,000 for New York. Factors that may impact the agreed upon salary within the range for a particular candidate include years of experience, level of education obtained, skill set, and other external factors.
Performance Bonus
Equity: Meaningful stock options so you share in our growth
Time Off: Flexible PTO
Health & Wellbeing: Comprehensive health coverage (US)
Retirement: 401(k) options depending on jurisdiction

About the Company

Auquan Ltd

AI-driven financial insights from unstructured data

Auquan provides AI-powered intelligence for financial services, focusing on private equity and ESG. Its Intelligence Engine searches unstructured data and uses retrieval-augmented generation to produce concise, real-time summaries and risk signals as an insights-as-a-service platform, with revenue from subscriptions and service fees. The system is tailored to finance, emphasizing ESG, reputational, and regulatory risks to deliver actionable insights rather than generic outputs. Its goal is to help financial firms make faster, more informed investment decisions while reducing downside risk.

Company size: 51-200
Company stage: Series A
Total funding: $22.1M
Headquarters: London, United Kingdom
Founded: 2018

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