AI Engagement Lead / Solution Architect New York - hybrid - Fulltime

AI Engagement Lead / Solution Architect to lead a GenAI engagement end-to-end for a Fortune 500 financial-services client — from POC through full-scale implementation. You will act as the single point of accountability for both the architecture and the delivery: you own the solution design and the key technical decisions, and you lead the client relationship and program to a successful, governed release. You will direct a team of Turing specialists (AI/ML engineers, integration and automation engineers) and partner with the client''s own architects, business analysts, and valuation SMEs.

This is an accountable architect-lead role. You are expected to make and own the right design decisions, set the guardrails, and govern delivery — leaning on your specialist engineers to build the deep components.

Required skills

• 10 years of professional experience, with 4 years driving Machine Learning / AI projects and solution design.

• Accountable ownership of solution architecture — able to set the target architecture, make and defend key design decisions, and be answerable for them to the client.

• Good working understanding of modern GenAI agentic designs and frameworks (e.g., LangChain / LangGraph) and of AI integration patterns, including MCP (Model Context Protocol)-style tool and data integration.

• Ability to define security boundaries and model/tool controls — what an AI system is allowed to access and do, guardrails against incorrect or unverifiable outputs, and human-in-the-loop checkpoints.

• Experience establishing delivery governance and release gates — clear quality, security, and compliance criteria that each release must pass before go-live.

• Understanding of Cloud services (Azure, Google Cloud Platform, or AWS) and Agile delivery with tools like JIRA and Confluence.

• Excellent communication and stakeholder management to collaborate with senior client SMEs; an entrepreneurial, founder''s mindset to own delivery end-to-end.

• Good to have: exposure to financial-services / asset-management valuation workflows or other regulated enterprise domains.

Roles & responsibilities

• Architecture & Technical Accountability:

• Own the solution architecture and the key design decisions across the platform and remain accountable for them with the client.

• Guide integration design, including MCP integration patterns and how the AI system connects to the client''s data and tools.

• Learn the client''s workflow well enough to translate business needs into a sound, practical technical approach.

• Define security boundaries and model/tool controls — access limits, guardrails, and human-review checkpoints that keep sensitive outputs correct and controlled.

• Provide technical direction to the engineering team on solutioning and system design; align them to a technical roadmap and ensure timely execution.

• Delivery Governance & Release Gates:

• Establish and enforce release gates — the quality, security, and compliance signoffs required before each release.

• Own overall delivery so scope, quality, and timelines are consistently met; manage the big-picture program timeline (releases, phases, go-live plans) using engineering velocity/capacity inputs from the EM.

• Ensure delivery decisions reflect cost, ROI, and long-term business impact.

• Identify delivery risks, create proactive mitigation plans, and track program health across all milestones.

• Ensure robust business-facing documentation — requirements/BRDs/PRDs, implementation plans, and roadmaps.

• Client Relationship & Communication:

• Lead discussions with the client to shape the AI roadmap and expand into new processes.

• Act as the primary point of contact for communication, feedback, and escalations; manage expectations proactively.

• Evaluate use-cases for new development and unlock new value for the client.

• Participate in the client''s internal stakeholder meetings to capture, clarify, and consolidate requirements into actionable product needs.

• Team Leadership & Coordination:

• Drive cross-functional alignment across engineering, product, and client teams.

• Remove blockers for client and internal teams through clear communication and effective prioritization.

• Conduct regular 1:1s focused on support, delivery alignment, and well-being.

• Acknowledge new client requests promptly and partner with the EM to assess feasibility, capacity, and timeline impact before committing.