Top Artificial Intelligence Jobs

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Customer-facing AI/ML Solution Engineer supporting strategic accounts through technical presentations, demonstrations, workshops, solution architecture, proof-of-concepts, MVPs, and customized prototypes. Partners with account and regional solution engineering teams to accelerate sales, drive AI/ML adoption, translate customer needs for product and engineering teams, and enable broader technical proficiency. The role also documents best practices and requires 30–40% travel.

The role involves building AI systems for customer support, creating automated insights, and operationalizing AI in workflows while collaborating with engineering and customer solutions teams.

Design, prototype and deliver full-stack banking solutions using Python/NodeJS/ReactJS. Build ML/GenAI and LLM-based features, APIs (OpenAI standards), reliable production services, databases (SQL/NoSQL), CI/CD pipelines, and provide 3rd-line support.

Leads global commercial data platform and AI engineering strategy for Innovative Medicine. Oversees data architecture, data products, cloud platforms, AI/ML, generative and agentic AI, MLOps, governance, and production delivery. Partners with commercial, medical, marketing, technology, and regional teams to create scalable data and AI capabilities. Builds and develops engineering organizations, manages strategic partnerships and innovation pilots, advises on governance and architecture, and drives measurable business value through enterprise data and AI investments.

Analyze and certify conversational AI queries and answers against source data. Improve prompts, semantic layers, taxonomies, metadata, and labeling in collaboration with Product Management, Data Science, Engineering, and QA. Investigate data relationships and recurring accuracy issues, write technical requirements, document business logic, support testing and release communications, and provide data-quality input to the product roadmap.

Develop, test, deploy, and support AI-powered applications, automation solutions, and agent-based systems. Translate stakeholder workflows into scalable technical solutions using Python, C#/.NET, TypeScript, React, Azure AI services, APIs, and orchestration frameworks. Implement RAG, prompt engineering, and agent workflows; maintain testing, documentation, security, monitoring, and support standards. Collaborate with engineering, architecture, legal, security, data, and business teams while contributing to pilots, user adoption, and continuous improvement.

Leads enterprise AI platform strategy, roadmap, governance, investment, and delivery across engineering, architecture, product, security, legal, risk, finance, and operations. Oversees complex platform programs, dependencies, KPIs, costs, scalability, ethical AI practices, and organizational adoption. Evaluates emerging AI technologies, manages ecosystem partnerships, and advises executives while translating technical strategy into measurable business outcomes.

Lead corporate development to source, evaluate, diligence, negotiate, and integrate acquisitions and investments. Build AI-driven sourcing and diligence systems, manage transactions, coordinate cross-functional teams, and drive post-acquisition integration and value creation.

Develops AI and machine learning software solutions that improve organizational efficiency and decision-making. Responsibilities include building applications in Python, Java, or C++, working with NLP, deep learning, or computer vision, deploying models to production, and supporting Linux or UNIX systems. Preferred experience includes cloud-native and containerized development, infrastructure as code, major cloud platforms, hybrid architectures, and defense or intelligence environments.