Applied AI Engineering Lead – VP, Markets Operations

Overview

In this VP role, you will design and lead agentic AI solutions that enhance Markets Operations workflows and controls. You’ll bridge AI capabilities with enterprise systems, delivering scalable, production-ready AI services and robust evaluation pipelines. Collaborating with engineers, researchers, and business leaders, you’ll translate high-value opportunities into impactful, compliant AI-enabled processes. You’ll shape architecture, governance, and continuous learning practices to advance safe AI in financial services.

Responsibilities

• Lead design and implementation of agentic AI applications for workflows, controls, exception management, and productivity
• Define scalable, secure AI engineering architecture patterns for production-ready AI/ML/GenAI solutions
• Design agent harnesses, orchestration layers, tool-use frameworks, and guardrails for enterprise AI
• Develop context management strategies including retrieval, memory, grounding, and lifecycle of contextual information
• Build AI services and infrastructure with APIs, event-driven patterns, CI/CD, IaC, observability, and testing
• Collaborate with researchers, data scientists, and software engineers to translate AI capabilities into reliable enterprise apps
• Establish evaluation, monitoring, and feedback mechanisms for AI systems and risk controls
• Design continual learning approaches with human-in-the-loop, telemetry, and safe release practices
• Translate stakeholder pain points into AI-enabled solutions with measurable business impact
• Document architecture decisions and engineering standards for technical and non-technical audiences
• Mentor engineers and foster a culture of technical excellence and responsible AI adoption

Key requirements

• Strong software engineering with Python; experience building/operating production-grade apps
• Experience designing/building AI/ML/GenAI/agentic apps integrated with enterprise systems
• Solid understanding of LLM patterns: prompt engineering, retrieval augmentation, tool calls, context management, evaluation, guardrails
• RESTful API design and integration; experience with FastAPI
• Data engineering concepts, ETL, and integration with enterprise data platforms
• CI/CD, automated testing, observability, production readiness
• Infrastructure-as-Code familiarity (e.g., Terraform); cloud/container deployment patterns
• Knowledge of database design and integration (relational, document, vector, graph)
• Security, controls, compliance, and model risk considerations for enterprise AI
• Strong communication and cross-functional collaboration skills

• Cross-functional collaboration
• Strategic stakeholder influence
• Technical leadership
• Python
• LLM applications and related patterns
• FastAPI

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