Top Artificial Intelligence Jobs

Support delivery of applied AI solutions by building low-code automations, integrating AI APIs (LLMs/vision models), assisting data pipelines, contributing to observability and cloud setups, documenting work, and communicating with technical and non-technical stakeholders in client-facing projects.

Design, build, and operate scalable cloud-native backend services and platform infrastructure (Kubernetes-based). Improve reliability, performance, deployment, observability, and developer tooling, and build AI-assisted internal tools and a coding agent to enable end-to-end feature delivery. Collaborate across teams and participate in architecture, debugging, and operational ownership.

Lead product vision and execution for personalization systems and a new customer-facing LLM-powered AI experience. Own roadmap, partner with ML/engineering/design, launch personalization and conversational AI features, run experiments and evaluations, and drive retention, conversion, and long-term revenue.

Design, prototype, and evaluate secure and robust AI/ML systems. Build models and data pipelines using modern ML frameworks, test defenses against adversarial attacks, collaborate with researchers and sponsors, and mentor junior teammates while transitioning capabilities to government customers.

Serve as the product expert and technical lead across sales cycles. Run tailored demos and POCs, handle security and integration questions, onboard customers, run and interpret admin reports, and feed field insights back to Product and Sales. Support internal training and contribute to customer retention.

As an AI Deployment Engineer, you will convert AI models into production systems, optimize performance, and work with teams to enhance user experience.

Enterprise Account Director responsible for outbound enterprise hunting, building pipeline, owning full-cycle SaaS sales from outreach to close, managing complex deals with senior stakeholders, driving renewals and expansions, and partnering with internal teams for account strategy and accurate pipeline forecasting.

Produce and edit technical multimedia assets and AI-generated videos from LXDs/SME storyboards. Build demos, screencasts, graphics, and job aids; ensure QA, branding compliance, and seamless platform integration; maintain organized media libraries and rapidly update content to match product releases.

The role involves integrating AI supercomputing systems into datacenters, collaborating with engineering teams, and producing documentation for processes related to datacenter integration and fit-out.

The Assurance Technology Director leads initiatives to modernize audit technology and improve operational efficiency while driving innovation and enhancing audit quality.

Lead and scale engineering and product development for a cyber, compliance, and GRC platform. Embed AI across products, own platform architecture, drive agile high-velocity delivery, ensure security and data integrity, translate regulatory frameworks into workflows, and build a high-performing engineering team aligned with business leaders.

Lead end-to-end RFP lifecycle and implement an AI-augmented proposal workflow. Build and maintain an answer corpus, operate and tune RAG/retrieval pipelines, coordinate SME review, and produce persuasive, compliant proposals. Track win rates and continuously improve systems to reduce SME review time and increase competitive win rates.

Architect and lead the enterprise AI platform roadmap, building scalable developer environments, multi-modal AI pipelines, agent orchestration, vector-data solutions, RAG/cognitive search capabilities, vendor ecosystems, and guardrails for model observability, drift detection, security, and responsible AI. Collaborate cross-functionally to accelerate adoption and embed AI across the enterprise.

Customer-facing role designing, building, testing, and deploying AI-powered communication agents. Translate customer needs into AI configurations, create prompts, train users, produce documentation, troubleshoot implementations, and develop reusable templates and best practices to support adoption and continuous improvement.

The AI Platform Engineer will own distributed training and inference operations using Ray on GPUs, manage the lifecycle of machine learning models, optimize performance, and integrate reinforcement learning. Key responsibilities include overseeing the Ray ecosystem, configuring training and inference systems, and ensuring quality and performance metrics are met throughout the model lifecycle.

Design and implement AI-driven test automation and performance engineering for cloud-native, distributed systems. Build CI/CD-integrated automation frameworks, use ML/LLM techniques for test generation and prioritization, establish performance baselines and observability-driven validation, and mentor teams within SAFe delivery trains to embed quality across the SDLC.

Lead design and implementation of cloud-native, microservices backend systems that power AI-enabled features (LLM assistants, RAG pipelines, recommendations). Build scalable REST and event-driven APIs, establish MLOps practices, ensure reliability/security/observability, mentor engineers, and collaborate with product, scientific, and infrastructure teams to productionize AI services in regulated scientific environments.

Work directly with customers to design, build, and deploy AI-powered planning solutions in Pigment. Translate business problems into scalable models and custom AI agents, prototype agentic AI use cases, drive adoption, train users, and feedback product and engineering. Own end-to-end implementations and define repeatable deployment patterns.

Work with customers to design, build, and deploy AI-powered planning solutions on Pigment. Translate business problems into models and custom AI agents, own end-to-end implementations, prototype agentic use cases, and bridge product, engineering, and customer teams to scale repeatable AI deployment patterns and drive adoption.

Lead end-to-end design, build, and deployment of AI-powered planning solutions in Pigment. Translate business problems into scalable models and custom AI agents, work directly with stakeholders to drive adoption, prototype agentic use cases, and communicate customer feedback to Product and Engineering. Train users and define repeatable frameworks to scale AI deployments across customers.

Lead design and development of integrated AI systems combining hardware, software, firmware, board, and silicon. Define system architectures, optimize performance, implement and benchmark solutions, and translate business opportunities into product specifications. Innovate in reinforcement learning, vision, simulation, and robotics while mentoring technical leaders and influencing Intel's AI product roadmap.

Own the product roadmap for physical AI agents, translate cutting-edge research into developer-facing features, build evaluation metrics and quality frameworks, engage with developers and enterprise users, shape the open ecosystem, and drive cross-functional execution across research, engineering, hardware, design, and GTM.

Lead end-to-end delivery of bespoke AI solutions for enterprise clients, owning requirements, prototyping, and production-ready specs. Design agentic, human-in-the-loop workflows, run client discovery and workshops, and mentor consultants while shaping practice methodology and repeatable solution patterns.

Lead technical strategy and delivery for IRC's program-facing AI portfolio. Manage and grow an engineering and applied-AI team, design and ship agentic systems, predictive analytics, and diagnostics for low-resource settings, oversee Azure-based high-availability architecture, DevOps/CI-CD, security and cost optimization, and partner closely with program leaders and frontier AI organizations to scale responsible, safe AI solutions.

Design, develop, and deploy generative AI and ML solutions (LLMs, NLP, transformers) for agent portals, onboarding, chatbots, and predictive models. Build MLOps pipelines, APIs/microservices, and automated workflows. Collaborate with data, product, and operations teams, instrument A/B tests and monitoring, and embed AI governance, bias mitigation, and compliance.