Top Artificial Intelligence Jobs

The AVP, AI Product Manager will lead product management for advisor-facing AI solutions, overseeing the development lifecycle, validating solutions, and ensuring compliance while maximizing business impact.

Leads research, design, development, testing, validation, and deployment of AI/ML prototypes for space and missile defense. Collaborates with customers and multidisciplinary stakeholders to define requirements and present solutions, mentors junior engineers, tracks milestones and budgets, supports proposals and thought leadership, and advances scalable AI/ML algorithms using large datasets.

Lead a small AI engineering team to design, build, and ship production-grade AI products end-to-end. Own architecture, data pipelines, backend systems, deployment, and iteration. Work closely with city partners, product, and data science to deliver LLM-powered, predictive, and real-time solutions and run technical scoping workshops.

Leads strategy, roadmaps, and delivery for AI/ML and Generative AI products supporting commercial, medical, and government affairs. Creates product requirements, manages backlogs, partners with technical and business teams, builds ROI cases, drives experimentation and launches, and oversees responsible AI governance, scalability, compliance, and post-launch optimization. Requires strong executive communication and experience managing enterprise AI initiatives.

Evaluate and compare AI-generated responses in Castilian Spanish for accuracy, relevance, clarity, instruction-following, and cultural nuance. Apply detailed annotation guidelines, document decisions, maintain quality across high-volume evaluations, and participate in training, calibration, and quality reviews to improve AI understanding of Castilian Spanish.

Design, build, and maintain scalable, resilient distributed systems and customer-facing technology platforms. Collaborate across the software lifecycle, participate in architecture sessions and code reviews, improve engineering practices, define products, and support reliability, security, monitoring, cloud infrastructure, DevOps, and application deployment. The role requires experience with modern programming languages, cloud services, microservices, databases, and generative AI or LLM-based applications.

Lead client engagements that identify, prioritize, and operationalize high-value AI opportunities. Responsibilities include process and value-stream mapping, stakeholder workshops, business-case and ROI development, AI platform evaluation, strategy and roadmap creation, KPI definition, senior-stakeholder advising, proposal support, and mentoring. The role bridges business and technology to guide AI investments, workflow improvements, automation, and organizational capability development.

Designs continuous AI/ML monitoring frameworks, including performance metrics, drift thresholds, alerting, and review processes. Defines guardrails for agentic AI adoption, covering access controls, action boundaries, human oversight, and auditability. Reviews vendor AI architectures for hosting, data flow, and embedded-AI risks. Maintains technical documentation and evidence supporting monitoring and guardrail controls.

Leads research, design, development, testing, validation, and deployment of AI/ML prototypes for space and missile defense. Interfaces with customers and stakeholders, translates mission requirements into technical solutions, mentors junior engineers, tracks milestones and budgets, supports proposals, and contributes to technical thought leadership. The role requires Python-based algorithm development, AI/ML expertise, large-scale data experience, and eligibility for a U.S. Government Security Clearance.

Owns the roadmap, backlog, requirements, and delivery of agentic AI platforms for document generation and cognitive search. Designs multi-agent workflows, RAG pipelines, prompt strategies, evaluation metrics, observability, and governance controls. Partners with engineering, regulatory, clinical, quality, and business stakeholders to expand use cases while ensuring traceability, citation accuracy, responsible AI, and compliance in a regulated environment.

Early-career engineer building agentic AI components under senior guidance: implement tool integrations, prompt templates, retrieval steps, tests, instrumentation, and evaluation scripts; participate in on-call and design reviews; collaborate with product and business stakeholders while learning agent frameworks, LLMs, and production engineering practices.

Leads AI enablement, advanced analytics, automation, process mining, and digital transformation across Global Clinical Operations. Develops analytics assets, low-code/no-code solutions, workflow automations, reusable AI frameworks, and platform capabilities. Evaluates emerging technologies, defines business requirements, supports proofs of concept, promotes responsible AI, and drives adoption through communities of practice, super-user networks, coaching, and stakeholder engagement. Partners with clinical operations, enterprise technology teams, vendors, and global matrixed organizations to improve clinical trial execution and operational performance.

Build and maintain mathematical optimization models for network planning, capacity allocation, routing, and freight operations. Develop production AI applications for process automation, document extraction, and decision support. Establish evaluation, validation, monitoring, and regression-testing frameworks for optimization and AI systems. Identify new business opportunities, prototype solutions, quantify impact, and scale successful initiatives. Partner with operations, pricing, sales, finance, engineering, and leadership to communicate trade-offs and deliver reliable analytical systems.

Optimize large-scale AI training and inference systems for throughput, latency, and cost. Responsibilities include GPU kernel optimization, distributed system tuning, memory and compiler optimization, workload profiling, model compression, production-scale LLM inference, measurement-driven debugging, and collaboration with cross-functional teams. The role also involves code and design reviews, mentoring junior engineers, and shipping reliable production solutions.

Build and deploy cloud-native generative AI and agentic AI solutions for federal missions. Support model selection, agent development, tool calling, response synthesis, CI/CD, infrastructure automation, container orchestration, production monitoring, performance optimization, documentation, and cross-functional collaboration with engineering, data, and stakeholder teams.

Design, build, and ship production AI agents integrated end-to-end into a TypeScript/Node.js/React stack. Apply ML judgment to product decisions, improve agent reliability and evaluations, and move quickly in a startup environment owning ambiguous problems.

Design and evaluate transformer-based models for structured and time-series credit data. Analyze internal representations, run ablations and hyperparameter sweeps, build research prototypes in PyTorch/TensorFlow, and collaborate with ML engineering to productionize interpretable, discriminative, and generative modeling advances.

Own and engineer an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, and ChatGPT. Build production AI agents, workflows, integrations, APIs, connectors, and RAG solutions using MCP and function calling. Administer platforms, preserve permissions and governance, evaluate system quality and safety, troubleshoot complex issues, establish technical standards, lead solution reviews, mentor contributors, and advise platform leadership on emerging AI capabilities.

Serve as the first point of contact for customer questions, reviewing AI-generated responses, triaging urgent payroll issues, and managing queue resolution. Create help content, identify recurring product problems, and partner with product and engineering on fixes. Over time, own AI support agents and knowledge systems while using AI coding tools to ship small product improvements. This is an in-person, high-ownership role based in New York City.

Lead and scale 3Cloud's Advisory service line and enterprise AI agenda. Set strategy, operating model, offerings, governance, and cross-functional alignment. Drive commercial performance, develop leadership, embed AI into advisory portfolios, and ensure measurable business outcomes. Sponsor major pursuits and establish repeatable go-to-market motions and operating discipline.

The AVP, Business Analytics & AI leads the execution of data, analytics, and AI strategies, driving measurable business outcomes and ensuring governance of data products and AI operations while partnering with business and technology leaders.

Leads enterprise HR AI and digital transformation strategy, owning HR data architecture, governance, quality, and AI readiness. Develops multi-year data and AI roadmaps, evaluates platforms and vendors, and advises HR leaders on capabilities, risks, and priorities. Designs data models, taxonomies, lineage, pipelines, and integration patterns to support workforce analytics, reporting, automation, and machine learning. Partners across HR, IT, Legal, Finance, analytics, and governance teams to ensure secure, compliant, scalable, and well-documented HR data foundations.

Own the technical development and administration of an enterprise AI collaboration platform spanning Slack, Google Workspace, Glean, Claude, ChatGPT, and related systems. Build production AI agents, RAG integrations, APIs, connectors, workflows, and automations using MCP and function calling. Ensure security, permissions, governance, reliability, testing, and monitoring. Lead architecture reviews, establish engineering standards, troubleshoot complex integrations, evaluate emerging capabilities, and mentor contributors.

Designs and leads cloud and application security solutions across DNS, HTTP servers, microservices, container infrastructure, and CI/CD pipelines. Defines security requirements, controls, encryption, access management, risk assessments, testing standards, and compliance measures. Analyzes security data and risks, integrates network security controls, develops scalable solutions, leads technical working groups, and communicates findings and recommendations to technical stakeholders, customers, and third parties.

Lead AI-driven organization and work redesign across the enterprise: perform task-level decomposition, design AI-era roles and career frameworks, map workflows, partner with technical and HR teams to implement structures, and build reusable tools and methodologies to scale AI-enabled workforce transformation.