AI Engineering, TIFIN.ai

ROLE OVERVIEW

TIFIN.ai is the AI operating layer for the wealth enterprise, a platform offering an agent library to address workflows across operations, investments and growth. Our north star is delivering better wealth outcomes for more people. We’re building production-grade multi-agent systems that power advisor copilots, investment intelligence workflows, and autonomous research capabilities. Our AI Engineers architect, build, and operationalize these systems at scale, pushing the boundaries of what agentic AI can do. We’re hands-on engineers focused on shipping reliable, enterprise-ready agentic AI systems into production.

PROJECTS

• Agent Advisor Copilot. Build a production-grade, multi-agent copilot for financial advisors that retrieves and reasons over client data using RAG and a persistent knowledge graph of clients and holdings, analyzes portfolio exposures and risk scenarios, generates personalized insights, enforces compliance guardrails, and drafts client-ready communications — all within a monitored, auditable architecture graded by automated evals and LLM-as-judge review.

• Deep Research & Workflow Agents. Design end-to-end AI workflows spanning client discovery, investment research synthesis, portfolio construction and optimization, and compliant meeting preparation — powered by deep research agents that reason over live web and internal data in ReAct-style loops — replacing fragmented tools with intelligent, autonomous systems.

• Agentic Infrastructure & Reasoning Stack. Architect a scalable multi-agent platform on LangGraph-style orchestration, with agent memory and state management, dynamic tool invocation, model fine-tuning pipelines, structured output validation, observability, fault tolerance, and automated evaluation — solving reliability, explainability, and regulatory challenges at scale.

WHAT YOU’LL DO

• Design and implement production-grade multi-agent systems and ReAct-style reasoning loops using modern agent frameworks and orchestration engines (e.g., LangGraph, Pydantic AI, Agent Harness, Tool-Calling, Code Execution)
• Build agent workflows that integrate RAG-based retrieval, agent memory, and knowledge graphs for grounded, long-horizon reasoning, fine-tuning models where prompting and retrieval alone fall short
• Establish evaluation and benchmarking frameworks, including LLM-as-judge pipelines, for multi-step reasoning accuracy, groundedness, hallucination mitigation, and financial correctness
• Design and enforce AI governance — audit trails, guardrails, and human-in-the-loop checkpoints — appropriate to a regulated industry
• Develop distributed agent services with strong observability and failure handling, and optimize latency, cost, and infrastructure decisions across model serving, vector/graph databases, and caching

WHAT YOU’LL BRING

• 3+ years of experience building and shipping Generative AI and LLM applications into production, with demonstrated experience designing and deploying multi-agent systems, including LangGraph or comparable orchestration frameworks and ReAct-style deep-agent reasoning loops
• Strong experience with RAG, agent memory, knowledge graphs and graph databases, and LLM fine-tuning
• Deep proficiency in Python, with experience in distributed systems, cloud infrastructure (AWS/GCP/Azure), and containerized deployments
• Experience implementing evaluation frameworks, benchmarking, and LLM-as-judge pipelines, along with monitoring and reliability safeguards for AI systems
• Strong systems thinking, an ability to design beyond single-model solutions toward coordinated, multi-component architectures
• Resilience and adaptability, driven by a desire to build state-of-the-art AI that drives better wealth outcomes for more people

COMPENSATION RANGE

$200,000 - $275,000 USD, competitive and appropriate to experience level