AI Lead Architect

AI Engagement Lead / Solution Architect

Location - NYC, NY-Hybrid- 3days - WFO

Full-time hiring

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, GCP, 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.