Applied AI Engineer

Location

New York, NY

Hybrid role requiring 4 days/week in the office in Bryant Park, with Wednesdays remote.

Compensation

$200,000 – $250,000 Base + Competitive Equity

Visa

Open to H-1B / OPT and other visa transfers
Open to most visa types; no new $100K H-1B sponsorship.

Company Stage

Bootstrapped – Post-PMF / Growth-Stage Technology Company

Industry

Artificial Intelligence, Financial Technology, Institutional Investing, Enterprise Software, AI Infrastructure, Quantitative Finance

About the Company

Our client is building AI-powered software and infrastructure that helps sophisticated institutional investors become AI-native.

The company develops intelligent platforms that combine agentic AI, LLMs, data orchestration, and financial workflows to help hedge funds and asset managers automate complex research, analytics, and investment processes.

The business has reached more than $10M in ARR without external venture funding, demonstrating strong product-market fit and a highly capital-efficient operating model.

The engineering team operates with a strong R&D mindset, continuously evaluating new AI technologies and incorporating them into production systems. Engineers have significant ownership over architecture, product direction, and implementation.

As an Applied AI Engineer, you'll build AI-powered applications and infrastructure directly used by investment teams, working across agentic workflows, MCP-connected systems, LLM applications, financial data pipelines, and full-stack product development.

This is an opportunity for a strong software engineer who wants to work at the intersection of modern AI and institutional finance while owning projects from 0-to-1.

What You'll Do

• Build AI-powered features directly into client-facing financial platforms
• Develop LLM-powered research intelligence and automation tools
• Build agentic workflows that automate complex investment and research processes
• Design and implement AI orchestration systems and agent pipelines
• Build MCP-connected tools and data sources for intelligent applications
• Develop internal and client-facing AI infrastructure
• Build AI observability systems and agent health monitoring capabilities
• Develop expert skills and reusable AI tooling for client implementations
• Build command-line tools and MCP servers that extend AI capabilities
• Design data orchestration systems connecting AI agents to financial datasets
• Build full-stack applications tailored to institutional investment workflows
• Develop backend APIs using Python and frameworks such as FastAPI
• Build responsive front-end interfaces using React
• Develop natural-language interfaces for complex financial datasets
• Build automated summarization and research intelligence systems
• Develop AI-powered workflow automation for hedge funds and asset managers
• Build ETL pipelines handling financial market data
• Work with positions, securities, risk metrics, and research signals
• Implement financial analytics and data processing workflows
• Work with time-series data and quantitative financial calculations
• Use tools such as Pandas and Polars for financial data analysis
• Deploy and operate applications within client environments
• Work with Kubernetes to deploy and maintain production applications
• Integrate LLM APIs and modern AI infrastructure into production systems
• Evaluate emerging AI frameworks and determine where they can create meaningful product value
• Build applications using modern agentic AI frameworks
• Work directly with investment teams to understand complex workflows
• Translate loosely defined financial problems into production software
• Ship customized platforms on compressed timelines
• Iterate quickly based on direct customer feedback
• Make architectural decisions across the AI and application stack
• Balance building from scratch with leveraging proven infrastructure
• Own projects from initial concept through production deployment
• Build software that compresses complex analyst workflows from weeks into seconds
• Continuously improve AI-powered applications based on real-world usage
• Establish reusable engineering patterns for AI implementations
• Help shape the company's long-term AI application and infrastructure strategy

Ideal Candidate Background

Experience Requirements

• 3–8 years of experience as an Applied AI Engineer, Software Engineer, Full-Stack Engineer, or related technical role
• Experience building user-facing AI products or features from 0-to-1
• Experience building production applications using modern AI technologies
• Hands-on experience with LLM-powered applications
• Experience building agentic AI products or workflows
• Experience working with AI APIs and modern AI tooling
• Strong full-stack software engineering experience
• Experience building customer-facing or user-facing applications
• Experience operating effectively in highly autonomous environments
• Experience translating ambiguous problems into working software
• Experience shipping production software on compressed timelines
• Experience collaborating directly with users or customers
• Strong product engineering mindset
• Experience working in startup or high-growth environments preferred
• Strong interest in institutional finance, hedge funds, asset management, or quantitative investing

Technical Requirements

• Strong Python engineering skills
• Python API development experience
• Experience with FastAPI, Flask, Django, or similar frameworks
• Experience building and integrating REST APIs
• Hands-on experience with LLM APIs
• Experience with agentic AI frameworks
• Strong understanding of the agentic loop underlying modern AI frameworks
• Experience with prompt engineering
• Experience building AI-powered user-facing applications
• Experience with MCP servers and MCP-connected tools preferred
• Experience with frameworks such as LangChain, LangGraph, Claude Code, OpenClaw, OpenCode, or similar tooling
• Strong React experience
• Strong JavaScript/TypeScript fundamentals preferred
• Strong SQL experience
• Experience building ETL and data pipelines
• Experience working with structured and unstructured data
• Experience with financial or time-series data preferred
• Experience with Pandas or Polars
• Experience with data orchestration and workflow systems
• Experience deploying applications using Kubernetes
• Experience working with Azure or comparable cloud infrastructure
• Strong API architecture and integration skills
• Strong software engineering fundamentals
• Ability to design scalable backend services
• Ability to troubleshoot complex production systems
• Ability to rapidly learn and adopt emerging AI technologies
• Ability to evaluate AI tooling based on practical product requirements
• Strong understanding of application architecture
• Ability to build reliable production systems using existing AI infrastructure
• Strong debugging and problem-solving skills

Education

• Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical field preferred
• Strong computer science and software engineering fundamentals
• Equivalent practical engineering experience accepted

Soft Skills

• Exceptional ownership and execution ability
• Highly autonomous working style
• Strong builder mentality
• Comfortable working in unstructured environments
• Strong first-principles problem-solving ability
• Comfortable solving loosely defined problems
• Strong product intuition
• Excellent technical communication skills
• Strong customer empathy
• Comfortable working directly with investment teams
• Strong curiosity around emerging AI technologies
• Genuine interest in how AI is transforming software
• Strong interest in financial markets and institutional investing
• Comfortable moving quickly from idea to production
• Strong bias toward action
• Comfortable making technical decisions independently
• Strong ability to iterate based on user feedback
• Comfortable working across backend, frontend, data, and AI systems
• Strong attention to engineering quality
• Comfortable building 0-to-1 products
• Comfortable learning unfamiliar technologies quickly
• Strong collaboration skills
• Low-ego working style
• Comfortable operating with limited structure
• Strong sense of urgency
• Comfortable working in a lean engineering organization
• Interested in working directly with sophisticated financial customers
• Comfortable working in-person in New York 4 days/week
• Excited about building software for institutional investors

Compensation & Benefits

• Base Salary: $200,000 – $250,000
• Competitive equity package
• Opportunity to join a bootstrapped company with $10M+ ARR
• Opportunity to work on production AI applications used by sophisticated financial institutions
• High ownership over AI-powered products and infrastructure
• Direct exposure to hedge funds and institutional investment teams
• Opportunity to build 0-to-1 AI products
• Exposure to cutting-edge agentic AI, LLM, and MCP technologies
• Opportunity to work across AI infrastructure, backend, frontend, and data systems
• Strong R&D-oriented engineering culture
• High autonomy and limited bureaucracy
• Direct impact on product architecture and technical direction
• Opportunity to work alongside engineers with backgrounds in leading financial institutions
• Hybrid work environment in New York City
• H-1B / OPT and other visa transfer support

Why Join

This is an opportunity to join a highly capital-efficient AI company that has already demonstrated strong product-market fit while operating with the speed and autonomy of a much earlier-stage startup.

You'll work directly on the intersection of agentic AI, modern software engineering, and institutional finance — building systems that help some of the world's most sophisticated investment teams automate research, analysis, and decision-making workflows.

Rather than maintaining mature systems or working within a narrow engineering function, you'll have the opportunity to build new AI-powered products from first principles and ship them directly to customers.

If you enjoy building 0-to-1 applications, experimenting with emerging AI technologies, solving ambiguous problems, working directly with users, and operating at the intersection of AI and quantitative finance, this role offers exceptional ownership and technical scope.