Top Artificial Intelligence Jobs

Own the architecture and hands-on development of production ML and LLM capabilities across an internal product portfolio. Build and evaluate models, multi-tenant AI services, security controls, monitoring, and reusable engineering patterns. Lead AI architecture decisions, mentor senior engineers, advise executives, and collaborate with product, security, legal, and compliance teams. The role is primarily hands-on, remote within the U.S. or hybrid in New York City, and offered as a 12-month contract.

Develop, deploy, maintain, test, and troubleshoot generative AI frameworks, services, applications, and models for ArcGIS Enterprise, ArcGIS Online, and cloud platforms. Collaborate with global cross-functional teams, ensure software quality and security, and communicate project status. The role requires Python or Java development, containers, Kubernetes, cloud environments, agile practices, and knowledge of generative AI.

Designs and delivers production-grade enterprise AI solutions using LLMs, RAG, agentic AI, MCP, and Azure. Translates business needs into secure, scalable architectures; establishes AI engineering standards, evaluation, observability, and guardrails; leads architecture and code reviews; mentors AI, software, and data engineers; evaluates emerging technologies; and supports client workshops, demonstrations, solutioning, and presales.

Assess, secure, and improve AI-enabled systems through secure architecture design, AI red teaming, vulnerability identification, adversarial testing, and mitigation development. Evaluate LLMs, RAG systems, AI agents, and multi-agent workflows; build security testing automation and tooling; implement controls; adapt open-source frameworks; and advise clients on AI security, governance, resilience, and responsible deployment.

Build and deploy production-grade generative and agentic AI applications using Python or JavaScript/TypeScript, LangChain, LangGraph, RAG, vector databases, Docker, Kubernetes, and cloud platforms. Develop workflows, integrations, evaluation pipelines, observability, security controls, and CI/CD processes. Optimize quality, performance, reliability, and cost while supporting production operations. Collaborate with architects, engineers, security teams, customers, and stakeholders; create documentation, participate in reviews, mentor engineers, and communicate technical tradeoffs.

Owns security engineering for AI and ML infrastructure, including training and inference pipelines, model registries, MCP servers, and agent runtimes. Designs security controls, conducts threat modeling and adversarial assessments, maintains compliance, and drives vulnerability remediation. Partners with AI Platform, Research, ML Operations, Science, and AI Safety teams to embed security across data, models, pipelines, and agentic systems.

Designs, develops, and secures production-grade generative AI platforms, LLM, RAG, and agentic AI applications. Builds model-serving infrastructure, orchestration frameworks, observability and guardrails, and cloud-native services. Implements AI security, governance, threat modeling, vulnerability remediation, secure deployment, CI/CD, and DevSecOps practices across cloud and hybrid environments.

Designs, builds, and evaluates secure generative AI applications, agentic workflows, and multi-agent systems. Responsibilities include threat modeling, prompt-injection and data-exposure assessment, identity and access controls, tool authorization, execution isolation, monitoring, auditability, and human approval controls. The role develops prototypes, architecture patterns, documentation, and evaluation methods while guiding engineers and leading design reviews. It requires strong Python, AI/ML, cybersecurity, cloud architecture, and governance expertise, with Secret clearance eligibility.

Build secure, scalable full-stack HR applications integrating enterprise AI services, LLMs, RAG pipelines, web technologies, and SQL data stores. Develop APIs, front ends, data tiers, retrieval and ETL pipelines, and AI-enabled workflows. Ensure data quality, privacy, role-based access, auditability, and performance. Collaborate with product, data, and security stakeholders to identify use cases and deliver reliable production solutions.

Lead customer deployments of frontier AI models across semiconductor and AI infrastructure workloads. Scope ambiguous problems, build and debug production software, design reproducible evaluations and benchmarks, validate performance, and support production deployment. Collaborate with customer engineers, researchers, and product teams while contributing to core product improvements. Create reusable tools and playbooks, communicate risks and results, and maintain delivery ownership across workstreams.

Leads and mentors an engineering team building AI-powered features for client-facing applications. Partners with Product and UX to define roadmaps, architects AI integrations, evaluates models and vendors, and ships reliable production software. Responsibilities include hands-on coding, AI evaluation and monitoring, safety and privacy controls, telemetry, progressive rollouts, testing, and cost and latency optimization. The role also promotes responsible use of AI-assisted development tools and translates ML capabilities into customer-focused product experiences.

Design and deploy production AI/ML systems, including data pipelines, RAG systems, agentic workflows, model integrations, APIs, observability, evaluations, guardrails, and cost optimization. Collaborate with product, design, and client stakeholders while contributing to architecture, code reviews, testing, deployment, and technical strategy. Build reliable, secure, scalable systems using modern LLM and cloud technologies.

Build and maintain full-stack internal network automation and AI-Ops products, including operational dashboards, topology visualizations, real-time data views, conversational AI interfaces, APIs, and data integrations. Integrate AI services, network and inventory platforms, and vendor tools. Use TypeScript, Python, CI/CD, testing, and cloud technologies while translating operational requirements into technical solutions and supporting globally distributed Agile teams.

Lead architecture and hands-on engineering for production AI solutions in financial services. Build RAG and agentic workflows, evaluation systems, LLMOps practices, security safeguards, and governed human-in-the-loop processes. Collaborate with clients, engineers, vendors, and risk teams to deliver reusable solutions, mentor technical staff, define implementation roadmaps, and support consulting pursuits.

Leads engineering delivery for Verizon’s enterprise AI platform, including an AI Gateway and Agent Development Lifecycle. Manages 3–5 engineers, owns the roadmap, operates Kubernetes across GCP and AWS, delivers model routing and LLMOps capabilities, establishes developer SDKs and self-service onboarding, manages FinOps and platform SLAs, and drives secure, scalable agent deployment and enterprise adoption.

Lead the architecture of Verizon’s enterprise AI platform across five layers, defining interfaces, standards, security, governance, observability, and scalability patterns. Conduct build-versus-buy evaluations and three-year TCO modeling, develop multi-cloud strategies, and architect LLM, agentic AI, and responsible AI capabilities. Create ADRs and reference architectures, guide reviews, and influence executive and engineering stakeholders. The role requires expertise in distributed systems, Kubernetes, LLM architecture, cloud platforms, API design, enterprise security, and agent orchestration.

Leads Verizon’s AI Connect sales strategy and performance, focusing on Dark Fiber, high-capacity networking, and AI connectivity solutions. Builds relationships with hyperscalers, data centers, AI vendors, and enterprise technology companies; executes go-to-market strategy; drives profitable revenue growth; and develops high-performing sales teams. The role requires strategic solution selling, alliance management, executive engagement, and cross-functional leadership in a complex matrix environment.

Leads business development for Verizon’s AI Connect portfolio, emphasizing Dark Fiber infrastructure and AI-enabled connectivity. Responsibilities include developing go-to-market strategy, driving revenue growth, managing C-suite and public-sector relationships, expanding service-provider partnerships, collaborating with sales teams, and building high-performing business development organizations. The role requires expertise in fiber infrastructure, networking, AI/ML transformation, executive solution selling, alliance management, and navigating complex matrix environments.

Leads Verizon’s AI Connect business development portfolio for Global Enterprise, with emphasis on Dark Fiber and AI-enabled connectivity. Responsibilities include managing a high-performing team, developing C-suite relationships, executing go-to-market strategy, expanding enterprise partnerships across key industries, driving profitable revenue growth, and collaborating with account teams. The role also requires building business development capabilities, mentoring talent, navigating matrix organizations, and integrating physical fiber infrastructure into enterprise AI connectivity and modernization strategies.

Leads enterprise IT, cybersecurity, and AI strategy across Vehlo. Oversees security frameworks, risk management, cloud and infrastructure operations, enterprise AI governance and platforms, technology integrations, budgets, vendors, facilities technology, and IT and security teams. Establishes responsible AI controls, manages enterprise AI licensing and adoption, and advises executive leadership on technology, cyber risk, and digital transformation.

Serves as the authoritative expert on secure, hardened Generative AI for a government AI test laboratory. Responsibilities include architecting and deploying AI agents and workflows, applying security hardening and federal compliance standards, evaluating models and frameworks, mentoring and training interns, documenting architectures and procedures, collaborating with technical and mission teams, and supporting curriculum development. The role requires Python development, expertise in LLMs, RAG, agentic AI, prompt engineering, and secure AI governance.

Build and scale AI-powered automations and internal tools across Operations, Finance, Marketing, and Customer Success. Translate business problems into pragmatic solutions, integrate LLMs, agents, RAG, APIs, and scripts, and harden prototypes for reliable production use. Coach non-technical employees, develop reusable automation components and templates, monitor workflows, document systems, and shape the company’s automation roadmap.

Designs and operates security controls for AI/ML systems, including threat detection, incident response, cloud and application security, model governance, adversarial testing, and security automation. Builds production tooling across AI platforms such as SageMaker, Azure OpenAI, and Databricks; applies MITRE ATLAS, OWASP AI Top 10, and NIST AI RMF frameworks; and integrates enterprise security platforms to protect models, data, pipelines, and AI applications.

Designs, builds, deploys, and monitors enterprise AI applications, APIs, RAG pipelines, predictive model integrations, and automated workflows. Develops secure, evaluated, and explainable AI services using governed data and cloud technologies. Collaborates with engineers, analysts, architects, product owners, and business stakeholders to translate models into practical decision-support tools while implementing responsible AI, privacy, access controls, testing, monitoring, and operational documentation.

Develops and deploys AI-enabled applications across internal technology teams. Responsibilities include monitoring emerging AI technologies, identifying use cases, rapidly prototyping solutions, integrating models with APIs and data pipelines, and guiding production deployment. The role owns AI initiatives end to end, evaluates accuracy and trustworthiness, improves reusable platform components, and delivers workshops on AI best practices. Strong full-stack, cloud AI, stakeholder management, and continuous learning skills are required.