AI Jobs in New York

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The Production AI Ops Lead is responsible for designing and developing the production lifecycle of full-stack AI applications, supporting system reliability, real-time inference observability, sovereign data orchestration, secure software integration, and resilient cloud infrastructure for international government partners. They own the production outcome, taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. They oversee the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components from APIs to UI, maintaining a responsive and production-ready environment. The role involves building automated systems to monitor model performance and data drift across geographically dispersed environments to ensure reliability, managing the technical lifecycle within diverse regulatory frameworks, and leading incident response for production issues in mission-critical environments to ensure rapid resolution and prevent recurrence. The lead also translates technical performance metrics into clear insights for senior international government officials and partners with Engineering and ML teams to influence the technical architecture and decisions of future AI use cases.

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The AI Deployment Engineering Manager leads the AI Deployment Engineering team in the Digital Native segment, focusing on ensuring the safe and effective deployment of Generative AI applications for developers and enterprises. Responsibilities include owning the strategy and operating model of the team to align with company objectives and customer needs, leading, building, and mentoring the team to deliver exceptional customer outcomes evidenced by production customer applications and increased API adoption. The role involves serving as the technical advocate for customers by synthesizing their needs to guide Research and Applied Product/Engineering roadmaps. The manager acts as the primary technical escalation point during development, maintaining direct communication with executive-level stakeholders and fostering trust. Additionally, the role requires serving as an industry thought leader and championing the safe and innovative application of the technology across various sectors. The manager oversees the entire implementation journey for strategic technology and software customers in the Americas, ensuring seamless platform integration, aligning technical teams to deliver a consistent and exceptional experience throughout the customer lifecycle, with success measured by live production applications, increased API adoption, and impactful customer stories.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. You will own the production outcome by taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will ensure full-stack integrity by overseeing the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components from APIs to UI to maintain a responsive and production-ready environment. You will build automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring reliability. You will manage the technical lifecycle within diverse regulatory frameworks and lead the response for production issues in mission-critical environments to ensure rapid resolution and build guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials and partner with Engineering and ML teams to ensure lessons learned influence the technical architecture and decisions of future use cases.

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Design agent systems from first principles including deciding the loop, tools, context strategy, evaluation harness, and system topology. Engineer the context by focusing on prompt construction, context windows, tool surfaces, structured outputs, and citation grounding. Drive evaluation rigor by building evaluations prior to agent construction, diagnosing failures, fixing root causes, and proving improvements through metrics. Use AI tooling such as Claude Code and Codex extensively to plan, scaffold, refactor, and debug work. Become a domain expert in healthcare claims, coding guidelines, and medical records as an integral part of the job.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, while supporting end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and the resilient cloud infrastructure required for international government partners. You will own the production outcome by taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. You will ensure full-stack integrity by overseeing the end-to-end health of the platform, ensuring seamless integration between the AI core and all full-stack components, from APIs to UI, to maintain a responsive and production-ready environment. You will scale the feedback loop by building automated systems to monitor model performance and data drift across geographically dispersed environments, ensuring the right levels of reliability. You will manage the technical lifecycle within diverse regulatory frameworks to navigate global compliance. You will lead the response for production issues in mission-critical environments as incident command, ensuring rapid resolution and building guardrails to prevent recurrence. You will translate deep technical performance metrics into clear insights for senior international government officials, and drive product evolution by partnering with Engineering and ML teams to ensure lessons learned in the field influence the technical architecture and decisions of future use cases.

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The role involves establishing a clear understanding of Harvey's platform and the process for building solutions, scoping, designing, and building custom agents, playbooks, and prompts for key strategic customers. It requires building strong relationships with senior stakeholders to understand their requirements, map their processes, and turn needs into working solutions. Responsibilities also include prototyping, testing, and iterating, including piloting Harvey features prior to general release, communicating market feedback to Product and Engineering teams to surface opportunities for impactful enhancements, partnering with Harvey's Legal Engineering and Go-to-Market teams to deliver measurable growth, and creating and maintaining reusable assets while capturing best practices to speed up future deployments.

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Build and maintain components of Clarium's master data enrichment pipeline, which classifies and enriches every product flowing through the platform; design and own classification and entity resolution workflows that combine deterministic logic and large language models (LLMs) for production data processing; build and operate evaluation harnesses, label sets, and regression suites to measure and improve pipeline quality; write production-level Python and SQL code; analyze complex datasets using statistics and machine learning to surface actionable insights and inform pipeline improvements; proactively audit data for quality issues, diagnose root causes, and implement fixes.

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Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.