Top Artificial Intelligence Jobs

Designs, builds, and deploys enterprise AI solutions for pharmaceutical and healthcare marketing operations. Leads client discovery, translates business needs into scalable architectures, develops integrations and AI workflows, and delivers production-ready cloud solutions. Responsibilities include generative AI, RAG, data pipelines, enterprise systems integration, authentication, observability, CI/CD, Docker, Kubernetes, and Google Cloud Platform. Serves as a technical advisor while collaborating with product, engineering, data, security, and delivery teams.

Build and deploy AI-powered workflows, agents, automations, dashboards, alerts, and recommendations for Compute and C3 capacity operations. Integrate CRMs, internal systems, and third-party APIs; automate repetitive operational processes; troubleshoot data and migrations; audit existing tooling; and prioritize new solutions. The role requires end-to-end ownership, rapid production delivery, strong systems thinking, and fluency with AI coding assistants, workflow platforms, APIs, and webhooks.

Leads US defense business development for autonomous sensing and edge-AI payloads, targeting DoD UAS programs and defense OEMs. Owns customer relationships, sales pipeline, capture strategies, proposals, demonstrations, flight trials, and strategic integrations. Translates military requirements into tailored technical solutions, builds government and industry partnerships, develops market intelligence, and secures major production or hardware integration contracts. Requires deep UAS expertise, DoD procurement knowledge, technical credibility, an active defense network, security clearance eligibility, and up to 50% travel.

Own customer onboarding, training, relationship management, technical issue resolution, retention, and success metrics across North America. Partner with sales, product, clinical, and internal teams to support remote cardiac monitoring programs, improve workflows, develop customer engagement playbooks, and influence product development. Lead customer-specific projects such as EHR integrations and travel to medical centers as needed, approximately 20%.

Senior software systems engineer responsible for high-level design and oversight of software, hardware, and firmware systems. Lead Agile development, ensure quality and security standards, mentor engineers, manage multiple projects, support procurement, perform technical reviews, generate performance reports, and integrate emerging technologies into defense test and training environments.

Coordinate program and mobile clinic operations for a rural health program: support project planning, logistics and inventory management, data collection and reporting, stakeholder coordination, and safe operation of mobile medical units to improve access to care.

Work with clients to analyze large, structured and unstructured data sets, design and test AI/ML models, mentor teammates, and deliver actionable insights to leadership, primarily supporting DoW/IC customers.

Design and operate Procore’s next-generation cloud compute and data platform in a FedRAMP-compliant environment. Build and support distributed, highly available systems; manage Kubernetes clusters, cloud-native infrastructure, and infrastructure as code; participate across the software development lifecycle; run technical proofs of concept; and collaborate with product, UX, IT, and engineering teams to shape platform capabilities and roadmap.

Ph.D. interns apply machine learning and AI to semiconductor chip design or enterprise engineering tools. Responsibilities include developing ML models for EDA automation, placement, timing, power, and verification, or deploying LLM, RAG, and agentic systems. Interns will train, evaluate, and deploy models, conduct rigorous experiments, collaborate with engineering and technology stakeholders, assess production performance and safety, and present findings to leadership. The role requires doctoral research in AI, machine learning, computer science, electrical engineering, data science, or a related field.

The Partner Solutions Architect will drive technical engagement with systems integrators, maintaining relationships with stakeholders to ensure successful AI solutions deployment and delivering strategic guidance.

Build and deploy AI-powered tools and workflows across performance marketing, creative production, CRM, landing pages, growth operations, and product growth. Automate analysis, personalization, content generation, campaign activation, reporting, and conversion optimization. Integrate LLM APIs, marketing platforms, data pipelines, and orchestration tools while mentoring growth teams on AI adoption and partnering cross-functionally to deliver production-grade solutions.

Lead AI adoption initiatives by understanding user needs, identifying barriers, preparing employees through practical guidance and learning, supporting launch, and measuring sustained workflow adoption. Partner with business leaders, managers, and implementation teams to embed AI-enabled processes into daily operations. Establish ownership, procedures, feedback mechanisms, and adoption metrics while sharing reusable change-management practices.

Leads the strategy, lifecycle, governance, adoption, and scaling of reusable AI solutions built by Global Distribution associates. The role identifies high-value use cases, improves and validates AI capabilities, manages the enterprise AI ecosystem roadmap, establishes responsible-use guardrails, and builds an internal community of AI creators and power users. It also translates emerging AI developments into practical business opportunities and partners with technology, data, risk, legal, compliance, and platform teams.

Lead the hands-on development of Vida’s secure data and AI/ML platform, including ingestion, data modeling, MLOps, model serving, feature pipelines, LLM integrations, DevOps, observability, and infrastructure. Partner with security and IT to protect sensitive health data and meet HIPAA/HITRUST-aligned controls while establishing scalable engineering patterns across the organization.

Build and lead production AI products, including agent orchestration, tool use, retrieval, evaluation systems, and customer-facing interfaces. Own projects from experimentation through scalable production, optimizing architecture, latency, cost, observability, reliability, and user experience. Shape technical and product direction, analyze agent failures, improve models and workflows, collaborate directly with customers, and mentor engineers across full-stack development.

Develop backend CRM/ERP functionality with Python and Django, support Camunda infrastructure, integrate LLM models, and build AI-related services. Design service architectures, make technical decisions, estimate and decompose tasks, conduct code reviews, collaborate with analysis, QA, and support teams, and coordinate development projects. The role requires expertise in PostgreSQL, APIs, high-load systems, Docker, Kubernetes, and CI/CD.

Lead co-design and build of production agentic and Generative AI systems with strategic ISV partners. Define objectives, prototype and ship RAG, multi-agent, long-horizon workflows on NVIDIA's stack, drive adoption, collaborate with Product/Engineering/Research, and influence product roadmaps. Ensure production inference, evaluation, observability, and enterprise deployment (MLOps/Kubernetes/Docker) with strong technical execution and partner-facing leadership.

Lead technical engagements with strategic ISV partners to design, prototype, and ship production agentic and generative AI systems. Drive adoption of NVIDIA stack (NeMo, TensorRT-LLM, etc.), set account objectives and timelines, coordinate cross-functional teams, and feed partner insights into Product, Engineering, and Research to shape roadmaps and deliver scalable enterprise deployments.

Lead the system architecture for production agentic AI capabilities, including orchestration, state and memory, tool integration, reliability, and guardrails. Write technical design reviews and standards, prototype architectural decisions, and partner with Data Science and engineering teams. Influence technical direction, review architectures, mentor senior engineers, promote AI-assisted development, and communicate trade-offs to executives and customers.

Supports client-facing Salesforce and AI implementations by gathering requirements, documenting processes, configuring and testing solutions, researching AI capabilities, and creating project documentation, training materials, and user guides. Collaborates with consultants and clients to ensure solutions meet business needs. This is an onsite Chicago internship for current college juniors graduating with a bachelor’s degree in Spring 2028.

Provides operational support and issue resolution for AI infrastructure and platform services. Manages Kubernetes and Docker deployments, monitors distributed workloads, and develops Python and Shell automation. Uses GitLab CI/CD, JIRA, ServiceNow, and Azure AD while troubleshooting networking and storage issues. Documents infrastructure processes and runbooks, collaborates cross-functionally, and participates in periodic on-call rotations, including occasional weekend coverage.

Leads enterprise-wide AI transformation, process excellence, Lean Six Sigma, automation, and continuous improvement initiatives. Partners with executives and cross-functional teams to redesign operating models, evaluate AI and RPA opportunities, establish governance, manage organizational change, and deliver measurable productivity and ROI improvements. Facilitates Kaizen workshops, develops business cases and KPIs, tracks value realization, and promotes enterprise adoption of digital solutions.

Develop and maintain scalable AI and ML architectures for a life sciences regulatory intelligence platform. Lead technical decisions, integrate LLM services and microservices, build conversational and agentic systems, manage data stores and databases, support production delivery, and collaborate in an Agile Scrum environment. The role requires hands-on Python development, generative AI deployment, cloud architecture, vector and graph databases, hybrid search, and backend API development.

Build and operate shared AI platform capabilities, APIs, SDKs, workflows, and reusable libraries for engineering teams. Develop reliable cloud-based AI and ML services using containers, orchestration, CI/CD, infrastructure as code, and strong observability. Support data preparation, deployment, monitoring, governance, security, and lifecycle management. Partner cross-functionally to productionize generative AI, retrieval, embeddings, and other machine learning use cases while improving developer enablement and operational reliability.

Own presales discovery, solution architecture, technical demos, prototypes, proof-of-concept definition, technical evaluations, RFP responses, and security questionnaires for enterprise AI customers. Translate customer workflows and technical requirements into validated architectures and measurable business value, manage implementation risks, and provide accurate delivery handoffs. Convert field insights into reusable reference architectures, demos, and product improvements.