Senior AI Engineer

Monstro is an AI-native fintech platform reimagining how people and institutions manage money. We’re building a modern foundation for financial decision-making—combining intelligence, automation, and elegant design to help users make smarter choices with confidence. Our team includes experienced builders from leading fintech, wealth management, and technology companies, united by a shared goal: to create a category-defining product that transforms how financial insight is delivered and acted on.

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.

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 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 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.

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.

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 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.

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 junior engineers on AI development and…