Top AI Automation Jobs in NYC, NY

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Contribute to building and hardening micro-apps and AI integrations on a shared GCP platform. Complete bug fixes and small features, participate in code and design reviews, apply security and software development best practices, monitor applications and follow runbooks, and use AI tools to assist development, testing, and documentation while learning from senior engineers.

Lead enterprise AI product strategy and roadmap, prioritize high-value GenAI and agent-based use cases, translate business needs into product requirements, and partner with data, engineering, architecture, and governance to deliver scalable, production-ready AI solutions that drive measurable business outcomes and adoption.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

Lead intake, roadmap, and governance for an AI automation/platform team. Harden and ship business-built AI apps to production with secure GCP platforms, define acceptance criteria and responsible-AI gates, manage vendor buy-vs-build decisions, drive adoption, measure outcomes, and manage AI economics and stakeholder communication.

The EVP, AI Transformation will lead BGB's AI-first operational model, redesign workflows, implement intelligent systems, and drive technology-enabled transformation across the organization for enhanced client value and competitive differentiation.

Support AI project teams by executing workstreams, preparing client-facing presentations and documentation, assisting implementation activities, maintaining required certifications, using relevant technology, communicating with stakeholders, and traveling as needed for client engagements.

Partner with Commercial teams to discover root causes, design and deploy end-to-end AI automation solutions, drive adoption and enablement, run hackathons, and maintain a prioritized roadmap while collaborating closely with Engineering.

Embed with Commercial teams to run discovery and root-cause analysis, design and build end-to-end AI automation solutions, deploy and drive adoption, enable AI fluency across teams through best practices and an AI Hub, run hackathons, collaborate with GTM Systems engineering, and maintain a prioritized Commercial AI roadmap.

Hands-on technical lead for a small team building AI-native, agentic automation systems. Code daily (Python/Flask, React) and design LLM-driven autonomous agents, ETL/data pipelines, MySQL schemas, and integrations. Own delivery, CI/CD discipline, code reviews, and enforce development standards while mentoring distributed team and ensuring compliance and governance.

Lead design and delivery of analytical and automation solutions across multiple domains. Own end-to-end implementations from requirements through deployment, apply AI/ML and RPA techniques (including LLM/GenAI), optimize performance, resolve issues, influence stakeholders, drive adoption, and mentor junior team members to deliver measurable business value.

Hands-on technical lead for a small team building AI-native, agentic systems and full-stack apps. Spend ~60–70% coding (Python/Flask, React) and 30–40% on technical leadership, code review, and delivery. Design agentic AI workflows, integrate LLMs into enterprise automation, own schema/ETL, CI/CD discipline, and governance compliance.

As an AI Automation Engineer, you'll architect CI/CD workflows, ensure pipeline reliability, and enhance productivity through AI-driven automation, collaborating across teams.

Lead development and scaling of a customer-facing AI agents platform. Own roadmap, prompt strategies, evaluation frameworks, and guardrails. Partner with engineering and OpenAI to deploy voice and automation models, improve automation rates and answer quality, and drive experiments using data and SQL.

Lead product development and scaling of Jerry.ai's AI agent platform. Define roadmap, design prompt strategies and guardrails, partner with engineering and OpenAI, run experiments to improve automation, answer quality, and customer satisfaction, and establish platform standards and evaluation frameworks.

Lead development and scaling of an agentic AI platform that automates customer sales and service using LLMs, human-in-the-loop feedback, and APIs. Drive roadmap, prompt strategies, evaluation frameworks, guardrails, and partnerships (e.g., OpenAI) to improve automation rates, quality, and customer satisfaction.

Lead development and scaling of a customer-facing AI agent platform: design prompt strategies, evaluation frameworks, guardrails, and integrations with OpenAI. Partner with engineering to improve automation, answer quality, and customer satisfaction through experimentation and production-grade deployment of LLM-based agents and workflow automation.

Lead end-to-end development and scaling of a customer-facing AI agents platform using LLMs and internal APIs. Define prompt strategies, evaluation frameworks, guardrails, and experimentation to improve automation rates and customer satisfaction while partnering with engineering and OpenAI.

Lead product development and roadmap for an agentic AI platform that automates customer sales and service. Partner with engineers on prompt strategies, evaluation frameworks, guardrails, and API/system design. Drive experimentation to improve answer quality, automation rates, and customer satisfaction, and work with partners like OpenAI to deploy advanced voice and workflow models.

Lead client engagements in insurance data, analytics, and AI: define roadmaps, run discovery sessions, design solutions, manage delivery and teams, develop proposals, and ensure DataOps, cloud (AWS), governance, and best practices across AI and analytics initiatives.

Lead AI-driven reconciliation and automation to improve encounter data quality. Own model performance, identify root causes, partner with technical and business teams to enhance models, prioritize roadmap, and scale automation while communicating results to stakeholders.

Designs, builds, tests, and deploys AI-enabled automations and copilots for GTM and RevOps. Creates reusable agent templates, prompt libraries, and orchestration patterns; implements end-to-end API integrations; monitors performance; documents and hands off production solutions; and trains/coaches business teams on safe, responsible AI usage and prompting best practices.

The AI Automation Engineer will apply AI in finance operations to automate processes like AP, AR, and reconciliation, ensuring compliance and performance measurement.

The Principal Consultant leads AI and digital transformation engagements, guides clients in identifying AI opportunities, builds strategies and roadmaps, implements solutions, and develops teams to achieve measurable business outcomes.

The role leads AI adoption across business functions, prioritizing use cases, building business cases, and driving organizational change necessary for successful implementation. It requires collaboration with IT leadership and the presentation of ROI to business stakeholders.