AI Jobs in New York

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The role involves taking full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies. Responsibilities include overseeing the end-to-end health of the platform to ensure seamless integration between the AI core and all full-stack components, from APIs to UI, maintaining a responsive and production-ready environment. The job also requires building automated systems to monitor model performance and data drift across geographically dispersed environments, managing the technical lifecycle within diverse regulatory frameworks, leading the response for production issues in mission-critical environments, ensuring rapid resolution and prevention of future issues. Additionally, the role requires translating deep technical performance metrics into clear insights for senior international government officials and 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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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support end-to-end system reliability, real-time inference observability, sovereign data orchestration, high-security software integration, and resilient cloud infrastructure for international government partners. Responsibilities include owning the production outcome with full accountability for long-term performance and reliability of AI use cases across international government agencies, ensuring full-stack integrity by overseeing all platform components from APIs to UI for a production-ready environment, building automated systems to monitor model performance and data drift across dispersed environments, managing the technical lifecycle within diverse regulatory frameworks, leading incident response in mission-critical environments with rapid resolution and prevention guardrails, translating technical performance metrics into clear insights for senior government officials, and partnering with engineering and ML teams to influence the technical architecture and decisions for future AI use cases.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers. Have complete ownership and autonomy in building and shipping best-in-class AI agents, from initial implementation through continuous iteration, working directly with leaders across industries like finance, healthcare, and hospitality to solve their users’ needs with reliable and intuitive AI agents. Dive deep into complex system challenges and build elegant solutions that scale to millions of users.

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Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may design graduate- and industry-level automotive engineering problems grounded in real practice; evaluate AI-generated solutions for correctness, assumptions, and engineering logic; validate analytical or numerical results using Python (NumPy, SciPy, Pandas); improve AI reasoning to align with first principles and accepted engineering standards; and apply structured scoring criteria to assess multi-step problem solving.

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As a Production AI Ops Lead, you will design and develop the production lifecycle of full-stack AI applications, support 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 take full accountability for the long-term performance and reliability of AI use cases deployed across international government agencies, oversee the end-to-end health of the platform ensuring seamless integration between AI core and full-stack components, build automated systems to monitor model performance and data drift across geographically dispersed environments, manage the technical lifecycle within diverse regulatory frameworks, lead response for production issues in mission-critical environments ensuring rapid resolution and prevention, translate technical performance metrics into clear insights for senior international government officials, and partner with Engineering and ML teams to ensure field lessons influence future technical architecture and decisions.

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As a Forward Deployed Engineer Strategist, you will work with Engineers, Product Designers, and other Strategists to deploy voice AI technology to solve customer problems. Responsibilities include meeting with strategic customers to understand their audio and voice AI needs, identifying relevant use cases through engagement with customer problems, designing and architecting custom integrations, guiding customers on best practices for AI model implementation, presenting results and proposals to technical and executive audiences, collaborating with Research and Product teams to incorporate field insights, building and delivering demos of AI technology, scoping potential applications in new industries, taking full ownership of major projects for strategic partners, and working daily with customers' engineering and executive teams to ensure optimal implementation of ElevenLabs' technologies.

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Design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); improve AI reasoning to align with industry-standard logic; apply structured scoring criteria to multi-step problems.

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Contributors may design rigorous statistics problems reflecting professional practice; evaluate AI solutions for correctness, assumptions, and constraints; validate calculations or simulations using Python (NumPy, Pandas, SciPy, Statsmodels, and Scikit-learn); improve AI reasoning to align with industry-standard logic; and apply structured scoring criteria to multi-step problems.