Top Artificial Intelligence Jobs

Build and own finance software, AI agents, forecasting and FP&A tools, billing pipelines, financial data layers, and automated close workflows. Reconcile usage-based robot billing with operations data, model pricing and unit economics, and implement controls and audit trails. This embedded engineering role partners closely with finance to replace spreadsheets, scale processes, and improve financial reporting.

Supports red team operations through adversary emulation, assumed-breach testing, purple team exercises, and AI security assessments. Tests prompt injection, AI guardrails, data access, developer copilots, automation platforms, APIs, identity, and permissions. Researches attacker techniques targeting applications, CI/CD, and LLM systems; contributes to playbooks, evidence collection, reporting, and defensive collaboration. Also develops custom ML/LLM models and applies AI to improve cybersecurity controls and threat response.

Serve as Helix’s technical authority on AI infrastructure and lead end-to-end technical diligence for data center investments. Evaluate electrical, mechanical, cooling, rack-level, vendor, supply chain, and manufacturing considerations; engage hyperscalers and chip vendors on next-generation designs; assess durable technology trends; and advise on productization, scaling, partnerships, and acquisitions. Translate technical roadmaps and trade-offs into investment and business decisions while building senior ecosystem relationships.

Designs, builds, deploys, and optimizes production AI applications combining graph databases, knowledge graphs, LLMs, and orchestration frameworks. Advises strategic customers, translates business and data challenges into scalable architectures, leads stakeholder engagements, and ensures successful delivery. Develops production code, integrates customer systems, evaluates application performance, and shares expertise through workshops, training, documentation, and thought leadership. Requires deep expertise in enterprise architecture, cloud platforms, generative AI, data engineering, graph technologies, and stakeholder management.

Designs and develops production-ready AI applications powered by large language models. Responsibilities include building backend orchestration services, user interfaces, LLM API integrations, prompt engineering workflows, RAG architectures, enterprise data connectors, and scalable microservices. The role also evaluates AI performance, supports responsible AI practices, prototypes conversational tools, and collaborates with product, data science, and business teams to deliver AI-enabled solutions for a global manufacturing company.

Leads enterprise AI innovation through hypothesis-driven pilots, evaluation, and experimentation with agentic workflows, RAG, text-to-SQL, and emerging LLM techniques. Designs secure, observable, cost-aware AWS architectures, develops reference implementations, and transitions successful prototypes into scalable enterprise patterns. Communicates technical findings, business impact, tradeoffs, and recommendations while evaluating AI tools, frameworks, vendors, and research.

Own the full client lifecycle for telehealth businesses, from onboarding through renewals and expansion. Build client relationships, troubleshoot platform and workflow issues, monitor engagement and churn risks, and deliver demonstrations and strategic recommendations. Use ChatGPT, Claude, Gemini, and other AI tools to research needs, automate workflows, improve communications, and validate solutions. Collaborate cross-functionally to enhance client experiences, document successful AI processes, and support clients during weekend work.

Own growth and revenue expansion across existing customer accounts through upselling, cross-selling, platform adoption, and strategic account planning. Use AI tools, LLMs, customer data, and market insights to identify opportunities, personalize outreach, manage expansion pipelines, forecast revenue, and improve customer growth outcomes. Collaborate with Client Success, Product, Engineering, and Marketing while developing repeatable growth playbooks and AI-assisted workflows.

Researches, develops, trains, and evaluates reinforcement learning algorithms for defense modeling, simulation, and decision-support applications. Builds Python and MATLAB software, custom simulation environments, reward and action/observation designs, reproducible experimentation pipelines, and integrations with C++ simulation systems. Uses Ray RLlib, GPU and distributed computing, and analyzes algorithm performance. Collaborates with software engineers, simulation developers, and subject matter experts to transition research concepts into operationally relevant prototypes.

Designs and delivers customer-facing API integrations, ETL pipelines, webhook automations, and AI-assisted workflows for nonprofit organizations. Owns discovery, architecture, development, testing, deployment, documentation, monitoring, and iteration. Productizes repeatable solutions into scalable service offerings, collaborates with Sales, Customer Success, and Product, and contributes to scoping, pricing, revenue reporting, and product feedback.

Lead GoDaddy’s Agent Name Service partnership ecosystem, managing partners from initial engagement through proof of concept, technical integration, launch, and adoption. Build relationships with executives, technology companies, standards organizations, and open-source communities. Coordinate cross-functional product, engineering, security, and business development workstreams, establish repeatable partnership processes, represent ANS externally, and use partner insights to shape product strategy and open-standard governance.

Support brand, campaign, and experiential creative projects through visual research, presentation and deck design, visual narratives, and AI-assisted image and motion exploration. Collaborate with creative teams, attend critiques and client presentations, contribute to studio projects, and develop skills in typography, layout, composition, art direction, Figma, Adobe Creative Suite, and emerging AI tools.

Design, build, and deploy intelligent AI assistants and multi-step agents using Google Cloud, Vertex AI, and the Google ADK. Responsibilities include prompt engineering, RAG, tool and function calling, responsible AI, cloud-native architecture, evaluation, monitoring, and production integration. The role partners with product, data, and business teams to deliver scalable conversational AI solutions.

Designs and deploys security controls for AI applications, agents, copilots, model platforms, and enterprise AI infrastructure. Builds APIs, services, policy checks, automation, and integrations governing identity, authorization, secrets, data access, tool use, logging, and egress. Performs AI threat modeling, security reviews, adversarial testing, and remediation for prompt injection and unsafe tool execution. Integrates security telemetry and response workflows while mentoring engineers and influencing enterprise security practices.

Leads Kyndryl’s AI Security function, securing internal AI platforms, applications, copilots, agents, models, and frontier AI initiatives. Responsibilities include building and developing the team, defining strategy and operating models, establishing reusable security controls, overseeing threat modeling and adversarial testing, managing AI risk remediation, partnering across technical and business functions, and briefing executives on security posture, risks, investments, and decisions.

Own an AI-native platform area end to end, from customer discovery and data analysis through prototyping, delivery, and business outcomes. Partner closely with restaurant operators, marketers, engineers, and AI agents to build data-driven products involving loyalty, marketing automation, analytics, integrations, and AI capabilities. Create precise specifications, validate prototypes, direct agent-built features, and improve the organization’s AI-native product development workflows.

Leads the design, development, and operation of production AI applications using LLM agents, RAG pipelines, AWS cloud services, Kubernetes, and modern full-stack technologies. Owns retrieval quality, evaluation, observability, infrastructure as code, CI/CD, production troubleshooting, and technical mentorship. The role requires shipping reliable AI systems, influencing architecture and business priorities, and collaborating across engineering and business teams.

Intern will analyze Salesforce pipeline data, build predictive revenue forecasts, segment and score opportunities, identify leading indicators and risks, and create repeatable data pipelines and analytical processes. The role applies statistical modeling, machine learning, AI, and visualization tools to support P&L analysis and business planning. Responsibilities also include translating technical findings into actionable recommendations, prototyping LLM and agentic AI applications, and refining models through monitoring and backtesting.

AI and Revenue Operations intern developing AI-powered applications, automated workflows, billing validation tools, reconciliation solutions, dashboards, and data-quality tools. The role involves mapping business processes, identifying automation opportunities, detecting exceptions and revenue leakage, collaborating with stakeholders, testing solutions, and researching emerging AI technologies. Work is primarily focused on improving operational efficiency and accuracy using enterprise platforms and automation tools.

Designs, implements, and optimizes Microsoft 365 security, compliance, identity, and governance solutions for clients. Configures Microsoft Purview, Entra ID, Defender, DLP, information protection, access controls, and AI-agent security capabilities. Assesses environments, identifies gaps, develops remediation plans, automates administration with PowerShell, and delivers documentation, workshops, testing, and knowledge transfer. The role requires strong consulting and customer-facing skills while supporting technical project delivery across enterprise environments.

Develop and support cloud-native Python backend services, RESTful APIs, integrations, and data solutions. Help move AI/ML models and pipelines into production for governance, risk, and compliance applications. Write automated tests, troubleshoot development and production issues, and collaborate with product, architecture, DevOps, and infrastructure teams in an Agile environment.

Leads and grows a North American AI/ML engineering organization of 6–8 engineers. Owns end-to-end delivery and production health of agentic AI, generative AI, and ML products, including data pipelines, model services, APIs, applications, and dashboards. Establishes engineering standards, partners with product and program teams, coordinates globally, supports hiring and career development, and remains hands-on in architecture and code review. Requires strong software engineering leadership, production ownership, Python, cloud, modern data platforms, and practical LLM and agentic-system experience.

Own the multi-year strategy, architecture, governance, reliability, monitoring, and cost optimization of EisnerAmper’s Azure-based AI platform. Establish enterprise standards for model lifecycle management, LLM evaluation, routing, security, compliance, and agent deployment. Build shared observability and enablement capabilities, manage vendor and model portfolios, deliver executive dashboards, and mentor technical and business teams while scaling the platform across Tax, Audit, Advisory, and Outsourcing.

Lead complex customer engagements from discovery through production delivery by architecting, building, and deploying AI solutions. Write and review production code, guide senior engineers, make architecture and deployment decisions, and translate customer-specific learnings into scalable CoCounsel product capabilities. The role requires hands-on expertise in generative AI, LLM systems, enterprise integrations, distributed systems, cloud architecture, security, reliability, and production operations, along with strong technical leadership and customer communication.

Build and deliver enterprise integrations connecting AI solutions with tax and audit products, ERP systems, document platforms, and data warehouses. Develop document-processing, retrieval, embedding, and vector-search pipelines; enable AI agents through APIs and MCP; and establish data validation and quality controls. Implement secure authentication, testing, monitoring, deployment patterns, and operational runbooks. Partner with clients and cross-functional engineering, platform, consulting, and reliability teams to deliver reusable, production-ready solutions.