AI deployment architect - New York

MakiPeople•New York, NY

About The Position

We are hiring AI deployment architects to configure, deploy, and evolve our agents for enterprise customers. This role sits at the intersection of product, engineering, and customer delivery ; you will translate business requirements into robust configurations, ensure each deployment reflects client needs, and iterate rapidly to maintain high performance and system stability. You will be a primary technical partner to our customers, guiding them through configuration decisions, troubleshooting complex behaviors, and shaping how they design and operationalize AI-driven screening. You will also act as a critical feedback conduit between the field and the product team, surfacing patterns that should be platformized and validating new capabilities with real-world customers. This is a highly technical & hands-on role. As deployments accelerate across industries and geographies, you will help define the standards, tools, and best practices that make our agents scalable. What makes this role unique You sit closest to real-world usage; your insights will directly shape how our agents evolve. You bridge product and customer needs, ensuring enterprise deployments remain robust, predictable, and high performing. You influence how AI-driven hiring is operationalized across the world’s leading companies. You will help define repeatable playbooks, tools, and standards that allow the deployment function to scale. As the first hire fully dedicated to this function, you will shape the boundaries of the role itself and set the industry bar for what excellence looks like in full-deployment engineering for conversational AI.

Requirements

Graduated from top engineering school or business/engineering dual-degree
Prior client-facing technical experience, ideally supporting enterprise implementations.
Hands-on work with conversational AI, LLM prompting, applied ML models, or workflow-based configuration.
Ability to debug state logic, model outputs, and configuration inconsistencies.
SQL proficiency for analytics and performance investigation.
Comfort with light scripting and structured configuration formats.
Strong ability to reason about system constraints and edge cases.
Ability to distinguish between local configuration work and product-level feature needs.
Structured approach to experimentation and iteration.
Clear and concise communication with both technical and non-technical stakeholders.
Ability to present, educate, and manage expectations during pilots and demos.
Confidence in advising customers and steering conversations.

Responsibilities

Build and adapt screening flows based on customer jobs and requirements.
Configure state prompts, tone parameters, voice selection, transitions, and conditional logic.
Set up and maintain custom vocabularies for ASR when relevant.
Prepare and run demos; support pilot implementations from start to finish.
Analyze conversation transcripts and identify sources of errors or drift.
Run isolated state tests for targeted debugging.
Iterate rapidly on prompts and configurations to improve performance.
Use SQL to investigate behavioral patterns, identify systemic issues, and validate improvements.
Advise customers on screening design, personas, and best practices for AI-driven interviews.
Communicate technical concepts, limitations, and trade-offs clearly.
Manage expectations during pilots; build structured feedback loops that drive continuous improvement.
Act as a trusted guide throughout deployment and iteration cycles.
Surface recurring field issues that should become productized solutions.
Contribute insights that shape new configuration surfaces, evaluation tools, and system-level capabilities.
Partner with product and engineering teams to test new features with selected customers and validate readiness for scale.