Top Artificial Intelligence Jobs

Lead design, development, deployment, and maintenance of large-scale Python-based generative AI platforms and services. Collaborate with data science, technical, and business stakeholders to define architecture, deliver production APIs, enforce Python standards and testing, drive CI/CD and monitoring, mentor developers, manage vendor partnerships, and ensure governance, risk, and controls alignment for enterprise GenAI solutions.

Architect and implement scalable AI/ML platforms, pipelines, services, and APIs for an enterprise modernization program. Responsibilities include model training and deployment, MLOps, cloud infrastructure, multi-tenant and multi-region architecture, monitoring, optimization, responsible AI, documentation, dashboards, and third-party ML tool integration. The role guides agile teams while ensuring federal security, privacy, governance, compliance, performance, availability, and cost-efficiency requirements are met.

Leads enterprise AI platform strategy, architecture, enablement, governance, FinOps, vendor relationships, and executive communication. Evaluates AI capabilities across cloud, productivity, and model providers; builds multi-year roadmaps; embeds AI across platform organizations; establishes risk, security, compliance, and cost frameworks; and drives adoption through influence across a large matrixed engineering organization.

The Solutions Managing Director in AI Tech Strategy & Consulting drives AI strategy engagements for enterprise clients, manages high-value portfolios, and leads consulting teams while fostering C-suite relationships and business growth.

Designs, develops, and scales enterprise process automation, custom applications, APIs, integrations, AI agents, and intelligent workflows. Partners with architects and business stakeholders to translate requirements into scalable Microsoft-based solutions. Responsibilities include integrating ERP, CRM, CPQ, databases, and cloud platforms; troubleshooting and optimizing software; participating in architecture reviews; documenting solutions; evaluating emerging technologies; and supporting governance, security, and compliance.

Lead prompt engineering for LLM-based solutions: design, test, and refine prompts; collaborate with domain experts and developers; integrate LLMs into applications; build a scalable prompt framework and knowledge base; analyze user feedback and guide tool/platform selection.

Design, develop, and optimize LLM prompts and a reusable prompt-engineering framework. Collaborate with domain experts and developers to integrate LLM solutions, collect user feedback, document best practices, and advise on tool/platform selection to improve business processes.

Design and optimize prompts for LLMs, collaborate with domain experts to translate business needs, integrate LLM solutions into applications, build a scalable prompt-engineering framework, gather and analyze user feedback, document best practices, and evaluate LLM tools and platforms to improve prompt effectiveness and reuse across business units.

Develop and optimize LLM prompts, build scalable prompt-engineering frameworks, integrate AI solutions into applications, analyze user feedback, document best practices, and collaborate with domain experts and software developers. The role also contributes to LLM technology and platform decisions across GE Vernova business units.

Owns technical program management for AI infrastructure resources, including prioritizing GPU and compute allocation, overseeing data pipelines, coordinating data operations, and managing vendor-supported annotation and QA. Partners with engineering leaders and executives to develop resourcing roadmaps, mitigate compute and data risks, improve utilization processes, and support AI robotics initiatives. The role also manages data procurement and contract negotiations while driving cross-functional program deliverables.

Lead AI Engineer driving AI-assisted engineering adoption, defining reference architectures, conducting reviews, building LLM-powered agents and developer tools, improving CI/CD and security practices, mentoring engineers, and enabling cross-team engineering excellence across Output Solutions.

The AI Product Manager identifies and prioritizes AI opportunities, translates client and business problems into testable hypotheses, and designs experiments to validate value before investment. They balance discovery and delivery, define success metrics, assess AI autonomy and human oversight, and collaborate with technology, data, security, risk, business, and product teams. The role also develops reusable practices for responsible AI adoption and drives organizational learning without formal authority.

Lead AI Engineer responsible for establishing engineering standards, architecture patterns, CI/CD and security frameworks, championing AI-assisted development, building LLM-powered agents, mentoring senior engineers, and driving quality, reliability, and delivery improvements across Output Solutions.

Build and maintain scalable Python-based AI applications under senior guidance. Assist with requirements, feature implementation, data modeling using Pydantic, code reviews, troubleshooting, testing, and CI/CD. The role requires collaboration, communication, and willingness to learn modern Python tools, frameworks, and libraries. Candidates should understand object-oriented programming, REST APIs, large language models, and AI coding tools. The position is fully remote within the United States.

Leads and scales OpenAI’s Applied AI Engineering team supporting startup customers. Defines the operating model, develops technical customer-facing engineers, advises startups on architecture and production deployment, partners with Sales and cross-functional teams, and turns recurring customer needs into repeatable playbooks, tooling, reference architectures, and deployment mechanisms. Serves as a senior escalation point while balancing customer impact, product priorities, platform strategy, safety, and team growth.

Lead and grow a team of AI Deployment Engineers to design, deploy, and scale secure, production-grade AI systems for enterprise customers. Own technical success across architecture, reliability, security, adoption, and expansion; act as senior technical escalation; partner cross-functionally to create reusable patterns, operating models, measurement, and product feedback loops while hiring and developing talent.

Lead architecture and development of custom business applications that automate legacy workflows for SMB clients. Translate business processes into scalable CRM, project-management, integration, and operational software using AI-assisted development through Replit. Ensure application security, reliability, infrastructure stability, and performance. Design integrations with legacy systems such as Sage 300, and eventually recruit and mentor a development team while establishing AI-augmented engineering practices.

Own the AI strategy and execution for Bullish and CoinDesk business teams. Build and deploy AI workflows, copilots, automation, and agentic solutions; identify and prioritize high-value use cases; establish self-service enablement through tools and training; define adoption, efficiency, and ROI KPIs; report outcomes to executives; and build the AI Enablement function and team from the ground up.

Leads enterprise AI market research and strategy for healthcare and financial services applications. Monitors emerging AI technologies, competitive investments, and industry trends; develops multi-year AI and IT roadmaps; applies forecasting and scenario analysis; and translates complex technical concepts into executive and Board-level recommendations. The role also builds and manages a specialized insights team, supports financial and strategic planning, researches new ventures, and aligns business-unit initiatives with corporate and CIO priorities.

Build and launch enterprise AI products using AI-assisted development, agentic systems, LLMs, RAG, workflow automation, and cloud-native architectures. Rapidly prototype strategic concepts, create production-ready solutions, develop reusable AI components, and identify automation opportunities. Partner with engineering, architecture, security, and platform teams to transition prototypes into production while shaping the enterprise AI roadmap and advising executives on emerging technologies and build-versus-buy decisions.

Leads the architecture, development, deployment, and optimization of agentic AI applications and autonomous systems. Builds Python backend services, secure APIs, RAG pipelines, microservices, and enterprise integrations using GenAI platforms and LLM frameworks. Establishes CI/CD, testing, evaluation, cloud-native deployment, resilience, and security practices. Provides technical thought leadership, mentors engineers, troubleshoots vulnerabilities and software issues, and manages technology risks and compliance.

Drive enterprise sales for NiCE Cognigy’s AI platform across the U.S. market. Build pipeline, generate ARR, manage direct and partner-led sales cycles, develop account plans, coordinate RFP/RFI responses, and close complex enterprise deals. The role requires engaging senior executives, communicating deal structures and ROI, collaborating with internal teams and channel partners, and representing Cognigy at industry events.

Own full-cycle recruiting for forward-deployed engineering and leadership roles across the US. Source passive AI and technical talent through LinkedIn, GitHub, communities, referrals, and talent mapping. Build Provectus’s talent community through meetups, workshops, hackathons, university programs, and industry events. Partner with hiring managers and senior leadership on hiring criteria, workforce planning, compensation benchmarking, candidate experience, and offer negotiation.

Own product areas within AI stockbroking and broader financial services, including defining user problems, product scope, metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial journeys. Use research, feedback, experiments, and performance data to make decisions, launch features, monitor results, and drive iterative improvements while balancing speed, usability, business impact, compliance, and operational complexity.

Design, build, and deploy production machine learning and LLM systems, including RAG pipelines, agentic workflows, fine-tuning, and embeddings. Own technical delivery from architecture through monitoring and iteration, collaborate with client engineering and product teams, mentor junior ML engineers, and help establish internal technical standards and best practices.