Senior AI Engineer

About the role

Location: SFO, CA or Chicago, IL (3days Hybrid) (Tue-Wed-Thursday)

Duration: 3 Months

Client : Uber

ROLE : W2

About the Role

We are seeking a highly experienced Senior AI Engineer with strong expertise in financial data, enterprise data platforms, and Generative AI/Agentic AI to support our H2 roadmap.

The ideal candidate will combine a strong foundation in SQL, enterprise financial data, and EPM systems with hands-on experience designing and building production-grade AI agents and multi-agent systems.

This role is ideal for someone who understands both the complexity of financial data and the engineering challenges involved in building reliable, scalable, secure, and auditable AI systems for enterprise financial workflows.

Key Responsibilities

Financial Data & EPM Foundations

Query, structure, transform, and integrate enterprise financial data across SQL databases and EPM systems.

Design reliable data pipelines and data access patterns for financial applications and AI-powered workflows.

Work with complex financial data including budgeting, forecasting, planning, reporting, revenue, expenses, and other enterprise financial datasets.

Ensure financial data is accurate, consistent, secure, and accessible to AI applications and agents.

Collaborate with finance and data teams to understand complex financial logic and translate business requirements into technical solutions.

Financial AI Agents at Scale

Design, develop, and scale production-grade AI agents for high-volume financial workflows.

Build AI systems capable of retrieving, analyzing, reasoning over, and acting on enterprise financial data.

Develop solutions optimized for accuracy, performance, latency, scalability, compliance, and auditability.

Integrate AI agents with databases, enterprise applications, APIs, data platforms, and other business tools.

Implement appropriate monitoring, evaluation, logging, and observability for AI agent performance and reliability.

Multi-Agent Orchestration & Sub-Agent Routing

Design and implement multi-agent architectures using supervisor-worker and hierarchical agent patterns.

Build systems that decompose complex financial problems into smaller tasks handled by specialized sub-agents.

Implement intelligent routing of tasks to the appropriate sub-agent based on the problem, context, and required expertise.

Design context-isolated workflows to ensure agents receive only the information required to complete their assigned tasks.

Coordinate multiple agents and tools to support complex financial analysis and decision-making workflows.

Agent Lifecycle Management & Controls

Build tool-augmented AI agents capable of interacting with databases, APIs, enterprise applications, and other systems.

Implement agent lifecycle controls, including event callbacks, pre-processing and post-processing hooks, and state management.

Design mechanisms for agent state persistence, memory, workflow recovery, and execution tracking.

Implement deterministic guardrails to control agent behavior and prevent unauthorized or incorrect actions.

Build human-in-the-loop workflows for high-risk, sensitive, or approval-based financial activities.

Ensure agent actions and outputs are traceable, explainable, and auditable.

Required Qualifications

7+ years of experience in software engineering, data engineering, AI/ML engineering, or a related field.

Strong hands-on experience with Python and SQL.

Proven experience working with enterprise financial data, financial systems, or EPM platforms.

Hands-on experience building Generative AI, LLM, or Agentic AI applications.

Experience designing and deploying production-grade AI agents or AI-powered data applications.

Strong understanding of multi-agent systems, agent orchestration, task decomposition, and sub-agent routing.

Experience integrating AI agents with databases, APIs, tools, and enterprise systems.

Experience implementing AI application controls such as guardrails, validation, observability, logging, and audit trails.

Experience with state management, agent memory, workflow persistence, or lifecycle management.

Experience designing human-in-the-loop workflows and approval mechanisms.

Strong understanding of enterprise security, data privacy, compliance, reliability, and auditability requirements.

Preferred Qualifications

Experience with AI agent frameworks such as LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar technologies.

Experience with EPM platforms, financial planning systems, or enterprise finance applications.

Experience with RAG, vector databases, embeddings, and enterprise knowledge retrieval.

Experience with cloud platforms such as AWS, Azure, or Google Cloud.

Experience with AI/LLM evaluation, observability, prompt engineering, and model governance.

Experience working with financial planning, budgeting, forecasting, accounting, or FP&A data.

Experience building enterprise-grade AI platforms or data agents at scale.

Ideal Candidate Profile

The ideal candidate is not simply a traditional Data Engineer or a traditional AI Engineer. We are looking for someone who has demonstrated experience in both areas:

Financial Data Expertise

SQL

Enterprise data platforms

Financial data

EPM systems

Data integration

Financial workflows

AI & Agentic Systems Expertise

LLMs and Generative AI

AI agents

Multi-agent orchestration

Sub-agent routing

Tool calling

Agent state and lifecycle management

Guardrails

Human-in-the-loop workflows

Production-scale AI systems

The successful candidate will be able to understand complex financial data and workflows while designing AI systems that are accurate, scalable, secure, compliant, performant, and auditable.