Top AI Automation Jobs in NYC, NY

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Design, develop, test, deploy, and maintain UiPath RPA solutions enhanced with agentic AI. Analyze business processes, gather requirements, document workflows, integrate systems, monitor automated processes, troubleshoot issues, and optimize solutions. Provide user training and support while collaborating with stakeholders and recommending AI and automation best practices.

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.

Designs and delivers production-ready AI, automation, integration, and agentic workflow solutions across business functions. Partners with stakeholders to identify process inefficiencies, translates needs into technical designs, builds prototypes and enterprise integrations, and deploys secure AI-enabled applications using Python, LLM APIs, RAG, orchestration frameworks, and cloud platforms. The role also supports adoption, governance, monitoring, documentation, security, and continuous improvement while measuring business impact.

Lead first-line risk and controls for Enterprise Data & AI, embedding risk management into technology delivery. Identify, assess, monitor, and report operational and technology risks for data, AI, platforms, and delivery pipelines. Partner with risk, compliance, product, engineering, and audit teams to evaluate controls, improve governance, and drive automation and continuous improvement.

Lead forensic technology engagements by designing and deploying machine learning models, statistical algorithms, and data pipelines for digital investigations and electronic discovery. Collaborate with clients to define goals, analyze cloud and enterprise data, develop reusable automation components, communicate findings to executive audiences, and deliver training. The role also involves managing people on high-profile consulting engagements and providing client-facing technical leadership.

Build and scale agentic AI systems that automate customer experience workflows across Slack and Salesforce. Analyze transcripts, detect SLA risks and escalations, provide self-service operational insights, and develop AI memory architecture. Partner with data, QA, workforce management, and customer advocacy teams to ensure system accuracy, reliability, trustworthy outputs, and measurable operational impact. Productize internal tools and design human escalation patterns for scalable deployment.

Build and operate an AI-driven global sourcing platform covering web crawling, data ingestion, normalization, entity resolution, product matching, provenance, and source monitoring. Integrate Zoho CRM and QuickBooks, apply LLMs with structured evaluation and human review, and identify automation opportunities across business departments. Lead pilots, train employees, measure impact, and scale the platform to hundreds of sources.

Leads enterprise AI transformation by developing the AI roadmap, prioritizing investments, managing initiative portfolios, and delivering measurable business value. Partners with executives and cross-functional teams to redesign operations, establish governance, evaluate technologies and vendors, drive adoption, and lead workforce change. Accountabilities include revenue growth, productivity, efficiency, customer experience, compliance, AI maturity, and value realization.

Hands-on technical leader who designs, builds, and deploys production-grade AI automation and agentic workflows. Responsibilities include rapid prototyping, RAG and document-intelligence systems, API integrations, orchestration, monitoring, reusable AI assets, and mentoring a small engineering team to deliver frequent releases and measurable business impact.

Design and author practical courses on AI and workflow automation adoption. Develop curricula, case studies, templates, exercises, and frameworks; translate implementation experience into actionable learning content; review technical accuracy; facilitate sessions; and teach responsible AI controls, workflow redesign, scaling, and value measurement.

Build and deploy AI-enabled automation solutions across enterprise systems. Partner with Business Applications, Data & Analytics, and IT teams to analyze processes, identify automation opportunities, integrate platforms, test and monitor solutions, document implementations, and train process owners. The role requires hands-on coding, AI workflow experience, enterprise integrations, scripting, SQL, structured data knowledge, production support, and strong communication with technical and non-technical stakeholders.

Develop and manage intelligent automation solutions using UiPath, bridging communication between project managers and clients, conducting process analysis, and providing documentation.

Build and deploy AI applications, agents, intelligent workflows, document-processing solutions, predictive analytics, and automation. The role spans AI use-case assessment, solution design, prototyping, evaluation, testing, production deployment, monitoring, governance, and responsible-AI controls. The engineer will integrate AI with enterprise systems, APIs, workflows, and data platforms while partnering with engineering, cybersecurity, governance, and mission teams in a federal environment.

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.

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.

Build AI agents and automations for internal business teams using tools such as Copilot Studio, Power Automate, Python, and AWS Bedrock. Identify manual workflows, design AI-assisted solutions, integrate them with Microsoft 365, AWS, and Salesforce, test with users, measure KPIs, and present results to technology leadership. Projects may involve document summarization, data extraction, intelligent routing, and natural-language querying.

Own and administer Salesforce and NetSuite while designing and deploying production-grade AI-driven automations and agent workflows. Ensure data governance, PHI/HIPAA compliance, release management, and quote-to-cash integrity. Partner cross-functionally to prioritize work, coach teams, document systems, and measure impact of automation on operations and revenue.

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.

Lead Net at Work’s transformation into an AI-native company by redesigning workflows, deploying virtual workers, building AI capabilities, training employees, and managing adoption. Establish the internal AI tool stack, standards, and use-case pipeline while delivering measurable operating leverage through reduced cycle times, improved quality, increased capacity, and margin gains. Validate results with Finance and drive organization-wide change through influence, process redesign, and strong stakeholder leadership.

Leads and grows a client-facing AI and data services practice, owning strategy, offerings, delivery methods, team development, client outcomes, sales support, and P&L performance. Guides clients from data readiness and AI use-case discovery through production deployment, adoption, and measurable business results. Advises executives, scopes complex engagements, builds delivery teams, and develops managed, subscription, and advisory services.

Design, implement, and optimize AI-powered workflow automation solutions. Analyze business processes, document current and future states, gather requirements, integrate automation tools, support testing and deployment, monitor performance, and identify improvements. Collaborate with business and technical stakeholders, provide training and troubleshooting support, promote automation best practices, and contribute to digital transformation and innovation initiatives.

Build and deploy automation workflows, AI-enabled tools, and production-ready applications using LLM APIs and agentic frameworks. Translate business processes into scalable technical solutions, integrate enterprise systems and SaaS platforms, and collaborate with operators, analysts, leadership, and central IT. Provide training and documentation, support security and governance reviews, and contribute to shared AI development standards across business units.

Lead North's integrations strategy and execution: prioritize, ship, and drive adoption of connectors and ecosystem tooling. Partner with customers and engineering to convert integrations into reusable platform capabilities (auth, connector framework, data sync, permissions). Define developer/partner experience (APIs, SDKs, plugins) and instrument analytics to measure adoption, time-to-stand-up, and retention.