Staff AI Engineer

As we scale our AI platform, we are building production-grade multi-agent systems that power advisor copilots, investment intelligence workflows, and autonomous research capabilities. We are seeking a Staff AI Engineer to architect, build, and operationalize these systems at scale. This is a hands-on, high-impact engineering role focused on shipping reliable, enterprise-ready agentic AI systems into production.

Advisor Copilot (Multi-Agent Systems) Build a production-grade, multi-agent copilot for financial advisors that retrieves and reasons over client data, analyzes portfolio exposures and risk scenarios, generates personalized insights, enforces compliance guardrails, and drafts client-ready communications — all within a monitored, auditable architecture.

Workflow Automation Design end-to-end AI workflows spanning client discovery, investment research synthesis, portfolio construction and optimization, and compliant meeting preparation — replacing fragmented tools with intelligent, autonomous systems.

AI Agent Platform & Infrastructure Architect a scalable multi-agent platform with orchestration engines, memory and state management, dynamic tool invocation, structured output validation, observability, fault tolerance, and automated evaluation — solving reliability, explainability, and regulatory challenges at scale.

Design and implement production-grade multi-agent systems using modern agent frameworks (e.g., Pydantic AI, Agent Harness, Tool-Calling, Code Execution)

Build agent workflows that integrate context retrieval, reasoning, tool execution, validation, and compliance checks,

Develop distributed services for agent execution with strong observability, monitoring, and failure handling

Establish evaluation frameworks for multi-step reasoning accuracy, groundedness, hallucination mitigation, and financial correctness

Implement memory management, context handling, and agent state persistence strategies

Partner with product, design, and engineering teams to translate business requirements into robust agent architectures

Optimize systems for latency, cost efficiency, and reliability in production

Contribute to infrastructure decisions around model serving, vector databases, caching, and orchestration layers

3+ years of experience building and shipping Generative AI and LLM applications into production, 6+ years of ML experience.

Demonstrated experience designing and deploying multi-agent systems of various architecture

Strong experience with multimodal LLMs, knowledge graph, data synthesis, LLM fine tuning, reinforcement learning, agent harness, agent memory

Deep proficiency in Python and modern AI frameworks

Experience with distributed systems, cloud infrastructure (AWS/GCP/Azure), and containerized deployments

Experience implementing monitoring, evaluation, and reliability safeguards for AI systems

Strong systems thinking — ability to design beyond single-model solutions toward coordinated, multi-component architectures

Resilience and adaptability - experience working at early-stage startups is a plus