Senior AI Software Engineer

About Monstro
Monstro is the operating system for governed financial intelligence. We build governance and intelligence infrastructure that enables artificial intelligence to operate safely, explainably, and at institutional scale.
We exist because the level of financial guidance historically available to a small group should be accessible to many more people. By combining AI with deep institutional infrastructure, we help financial institutions deliver more personalized, responsible, and life-changing financial support to millions of individuals.
We’re building mission-critical systems in a highly regulated domain, and we care deeply about doing it right. If you’re motivated by meaningful problems, high standards, and shaping infrastructure that improves financial outcomes, you’ll feel at home here.
About The Role
We're looking for a Senior AI Engineer who is, first and foremost, a strong software engineer—someone who writes clean, well-tested, production-grade code and brings the same engineering rigor you'd expect from any senior engineer on our team. On top of that foundation, you'll bring deep expertise in AI systems: building agentic workflows, deploying both commercial and open-source models, and designing intelligent features that work reliably at scale.
You'll build the AI-powered capabilities at the core of our platform—systems that can reason, plan, and act on behalf of users while remaining trustworthy, explainable, and aligned with user intent. Because we deploy within our partners' infrastructure, you'll work with both commercial model APIs (such as Anthropic's Claude) and self-hosted open-weight models, choosing the right tool for each use case based on performance, cost, security, and deployment constraints.
What You’ll Do
Build Production Software
Write clean, maintainable, well-tested code. Design APIs, services, and data models with the same rigor expected of any senior engineer. Participate in code reviews, contribute to architecture decisions, and uphold engineering best practices across the codebase. You'll own features end-to-end—from design through deployment and monitoring.
Design and Build Agentic AI Systems
Design and implement autonomous AI agents with planning, memory, and tool-use capabilities. Build orchestration layers that coordinate multi-step agent workflows, manage conversational context, and handle fallback behaviors gracefully. Integrate both commercial model APIs and self-hosted open-weight models depending on the requirements of each use case.
Deploy and Operate AI Models
Deploy and manage self-hosted open-weight LLMs within secure cloud environments. Optimize inference performance through quantization, batching strategies, and efficient serving frameworks (vLLM, TGI, or similar). Integrate commercial model APIs (Anthropic, OpenAI, etc.) where appropriate, managing cost, latency, and reliability. Build systems that can operate in environments with limited or no external network access.
Build RAG & Knowledge Systems
Design retrieval-augmented generation pipelines that ground AI responses in authoritative, up-to-date information. Develop chunking, indexing, and retrieval strategies optimized for financial content. Integrate AI systems with structured knowledge bases and real-time data sources.
Ensure AI Quality and Safety
Develop evaluation frameworks that measure reliability, consistency, and safety—not just accuracy. Build automated testing pipelines for AI systems, including regression testing, adversarial testing, and edge case detection. Implement guardrails that prevent harmful, biased, or off-topic outputs. Design transparency mechanisms and audit trails that support compliance and debugging.
Adapt and Fine-Tune Models
Adapt open-weight foundation models for domain-specific tasks using techniques like instruction tuning, LoRA, QLoRA, and parameter-efficient fine-tuning. Implement prompt engineering strategies and evaluate their effectiveness. Optimize model performance for latency, cost, and quality tradeoffs.
Monitor and Improve Production AI
Implement monitoring for model drift, latency, error rates, and output quality. Design for graceful degradation when models or services underperform. Create feedback loops that surface production issues and drive continuous improvement. Collaborate with Data Engineers and ML Engineers to ensure seamless integration with data pipelines and feature stores.
Mentor and Collaborate
Mentor junior engineers on AI development and software engineering best practices. Collaborate with product managers and domain experts to translate business needs into AI capabilities. Communicate complex AI concepts clearly to technical and non-technical stakeholders.
What We’re Looking For
Core Engineering Experience

7+ years of software engineering experience, with a track record of shipping production systems
Strong proficiency in Python and/or Go, with clean, well-structured code
Solid understanding of software engineering fundamentals: system design, API design, testing, code review, CI/CD, and version control
Experience with containerization (Docker, Kubernetes) and cloud infrastructure
Comfort owning features end-to-end, from design through deployment and observability
AI & ML Expertise

3+ years of hands-on experience building AI/ML-powered features or products
Experience building agentic AI systems—chat interfaces, multi-step workflows, tool-use patterns, and orchestration frameworks (LangChain, LangGraph, or similar)
Working experience with both commercial LLM APIs (Anthropic, OpenAI) and open-weight models (LLaMA, Mistral, Qwen, or similar)
Experience designing and implementing RAG pipelines with vector databases
Strong understanding of prompt engineering and LLM behavior optimization
Familiarity with model serving frameworks (vLLM, TGI, Triton, or similar) and inference optimization techniques
Experience with model adaptation techniques (LoRA, QLoRA, PEFT, instruction tuning)
Experience building evaluation and testing frameworks for AI systems
Mindset & Approach

A problem solver at heart—you dig into complex problems, explore options, and find practical solutions rather than waiting for direction
Proactive and self-driven; you identify opportunities and risks before they become blockers
Innovative and curious; you keep up with a fast-moving field and bring new ideas to the team
Passionate about building AI systems that are both powerful and trustworthy
Excellent communicator who can explain AI behavior and limitations clearly to any audience
Why Monstro?

Ownership & Impact: Shape the future of AI-powered finance—building a category-defining product used by consumers and institutions around the world.
Elite Team: Join a team with leadership that has a track record of scaling companies from early stage to major exits.
Principles-Driven Culture: Work in a culture that values speed, ownership, and impact—what most companies achieve in 90 days, we do in 45.
Comprehensive Compensation Package: Competitive salary, equity, and robust benefits package, including paid health, vision, dental, and disability coverage.
Note: This role will be hybrid in office for those in the NYC metro or remote for those in the Denver metro area (but with the expectation of periodic travel to our NYC office)
Base Compensation Range (New York City): $189,000 - $235,000
Base Compensation Range (Denver Metro): $166,000 - $207,000

The posted range reflects the base salary for this role across the market ranges for each location. Final compensation will depend on a variety of factors, including experience, skills, internal leveling, and market conditions, and will be offered within the stated range in accordance with applicable pay transparency laws.
Ready to Build With Us?
If you’re excited to contribute to a high-bar team building something meaningful, we love to hear from you!