Top Artificial Intelligence Jobs

Lightly AG seeks experienced Sales Subject Matter Experts to improve AI tools in sales workflows. Candidates should start soon with a commitment of 15 hours/week.

Lead clients in designing and deploying Microsoft Azure AI solutions. Oversee multiple projects, engage in client discussions, and support sales efforts.

Lead AI-driven transformation across corporate finance by mapping workflows, building a reusable finance knowledge infrastructure, prioritizing automation opportunities, delivering 5–10 high-impact AI initiatives annually, and partnering with Engineering, Data Science, and global finance teams to embed scalable AI solutions and enable change management.

The Senior AI Architect leads the design, development, and deployment of AI systems, ensuring efficient data interaction and governance across a large data platform.

Eight-week mentor-guided weekend program that provides hands-on enterprise AI workflow and automation experience. Participants build agentic systems, RAG, API-integrated workflows, and portfolio projects using tools like n8n, OpenAI, Airtable, and PostgreSQL, with deliverables such as BRDs, workflow diagrams, and prompt libraries.

Eight-week remote weekend mentorship program providing hands-on exposure to AI workflow automation, agentic workflows, n8n, API integrations, prompt engineering, RAG systems, and capstone projects focused on enterprise automation and documentation practices.

The Senior AI Scientist will develop innovative machine learning techniques for MRI, coordinating with teams and enhancing product capabilities through advanced model development and software maintenance.

The AI Solutions Associate supports AI initiatives, collaborates with teams to implement AI solutions, and promotes AI literacy while driving efficiency gains for clients and the agency.

Partner with Sales to lead technical discovery and scoping, design GenAI solution architectures, build bespoke demos and prototypes (RAG, fine-tuning, agents), author proposals and SOWs, capture reusable patterns into reference assets, and support handoffs to delivery teams. Requires up to 25% annual travel.

Design, build, and operate large-scale AI/HPC infrastructure spanning job scheduling (Beaker), runtimes, networking, and storage. Drive automation, performance optimization, root-cause analysis for distributed GPU workloads, mentor engineers, and collaborate with researchers to prioritize and deliver scalable orchestration and cluster health tooling.

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.

Design and develop AI-powered applications, implement agentic workflows using modern orchestration frameworks, and manage multimodal datasets while staying current with AI trends.

Design and develop full stack applications, integrate AI models, manage APIs, optimize performance, and collaborate with AI/ML teams.

Lead architecture and delivery of a neurosymbolic AI platform blending LLMs with constraint-based planning. Set AI strategy and governance, evaluate and integrate state-of-the-art models and vector infrastructure, resolve complex AI engineering roadblocks, and produce frameworks to enable large-scale, enterprise-grade autonomous planning.

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.

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.

Lead NVIDIA's storage strategy for AI Cloud, focusing on high-performance storage solutions for critical workloads, mentoring engineers, and influencing technical direction across teams and external partners.

Design, build, deploy, and maintain production AI solutions and agents using Python or .NET on Azure. Implement RAG pipelines, semantic search and vector DBs, work with large-scale data (Databricks/SQL), evaluate model performance, and collaborate with engineers and analysts to productionize experiments.

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.

Lead design and delivery of production LLM systems for drug discovery: build RAG pipelines, hybrid retrieval, text-to-SQL, agentic workflows, evaluation frameworks, and orchestration for scientific analyses.

The AI Engineer will develop and deploy AI/ML models, optimize data pipelines, and collaborate with teams to enhance investment decision-making using AI technologies. Responsibilities include maintaining AI governance and facilitating innovation workshops.

Lead AI initiatives for quantum system operations: define roadmap, prioritize bounded-AI pilots for calibration and readout, ensure model validation, observability, safety gates, and cross-functional execution to reduce calibration time, failed jobs, and recovery time.

The Senior AI Engineer will focus on building AI-driven pipelines, semantic technologies, and enterprise ontologies while mentoring and ensuring best practices in a hands-on role.

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.

The role involves building AI agents for healthcare practices, focusing on reliability, data pipelines, automation, and production systems.