Top Artificial Intelligence Jobs

Design and develop scalable AI platforms and developer tools using Python, MLOps, LLM prompting, and agentic workflows. Build ETL and data pipelines, integrate and deploy models on cloud infrastructure, implement monitoring and reliability dashboards, prototype generative AI applications, and establish testing, documentation, security, and ethical AI standards. Collaborate with data, engineering, and product teams to operationalize intelligent applications.

Leads enterprise semantic layer, data, and AI platform architecture. Defines lakehouse, medallion, data model, data contract, metadata, schema governance, feature store, model lifecycle, and interoperability standards. Partners with product, engineering, platform, and data science teams to create secure, reusable foundations for analytics, machine learning, and AI applications. Translates business and governance requirements into scalable technical architectures and provides thought leadership across enterprise data and AI enablement.

Lead product vision and roadmap for AI infrastructure and agentic systems. Work with engineering, data science, and sales to define requirements, prioritize features, enable execution, validate deliverables, maintain technical documentation, support go-to-market launches, and track customer feedback and market trends to drive platform strategy and measurable outcomes.

Lead technical direction for cloud platform engineering: design, build, and operate secure, scalable platforms supporting AI/ML and data initiatives. Drive DevEx, CI/CD, DevSecOps, observability, self-service developer workflows, and AI-assisted development including agent and MCP integrations.

Leads AI product strategy and MarTech enablement for marketing, from use-case prioritization and product development through production deployment, adoption, support, and ROI measurement. Oversees AI decisioning, orchestration, prompt engineering, responsible AI, and MarTech platforms while partnering with data, technology, privacy, legal, and agile delivery teams. Builds and mentors a multidisciplinary team, manages vendors, translates technical concepts for executives, and drives reliable PI planning and value realization across a matrixed organization.

Paid 10-week AI, data, and machine learning engineering internship. Interns will develop end-to-end data and AI/ML solutions, apply statistical and machine learning models, and contribute to projects involving incident resolution, computer vision, robotics, sustainability, and accessibility. Responsibilities include solution design, programming, debugging, testing, analysis, collaboration, and communicating technical concepts. The role is in person at Nike’s Beaverton, Oregon campus and includes relocation assistance.

Design and lead fairness and bias evaluations for AI-assisted People systems across hiring, performance, promotion, development, and workforce planning. Conduct algorithmic audits, subgroup and intersectional analyses, validation studies, counterfactual and generative AI testing, and human-AI workflow evaluations. Build scalable testing, monitoring, documentation, and reporting infrastructure. Partner with engineering, People, legal, privacy, and security stakeholders to recommend mitigations and communicate evidence, risks, limitations, and uncertainty to senior leaders.

Own concurrent healthcare customer implementations from contract handoff through stabilization. Manage stakeholders, phase gates, status reporting, discovery, workflow mapping, UAT, training, defect triage, and go-live reviews. Diagnose root causes using data and translate operational findings into AI agent configurations, routing rules, extraction fields, and escalation paths. The role combines project management, process design, customer partnership, and structured problem solving without production coding.

Supports analytics projects by understanding datasets, developing models, visualizations, reports, and actionable business insights. Collaborates with analytics teams, applies analytical tools to large datasets, adapts to changing business needs, and develops technical and leadership skills through a 12-week summer internship.

Lead end-to-end AI sector diligence: define key questions, design research, conduct expert interviews, synthesize findings to inform investment decisions, build methodology, and manage a 3–5+ junior-person pod at a founding-stage venture intelligence firm.

Build and maintain automated testing frameworks for distributed AI training and inference workloads. Develop end-to-end integration and stress-testing plans, validate GPU and hardware-software interactions, and troubleshoot failures in large-scale distributed clusters. Collaborate with systems engineers using telemetry and profiling tools to identify root causes and performance bottlenecks. The role requires Python automation, test framework development, containerization, and CI/CD experience, with Kubernetes, infrastructure automation, networking, and firmware testing experience preferred.

Develop, optimize, package, validate, and deploy AI/ML applications in secure, mission-critical environments. Collaborate with analysts and platform, infrastructure, and security teams; support MLOps, CI/CD, distributed systems, troubleshooting, performance tuning, accreditation, compliance, audits, and technical documentation. The role uses machine learning frameworks including TensorFlow, PyTorch, Scikit-learn, Keras, computer vision, and GANs.

Leads development and deployment of computer vision and AI/ML systems for real-time object tracking, monitoring, and intelligent data triage. Designs scalable architectures, agentic data-collection workflows, and production MLOps pipelines; mentors engineers, oversees data lifecycle and model deployment, and translates client needs into AI/ML roadmaps. Requires advanced Python, computer vision, Docker, Kubernetes, seven-plus years of machine learning experience, U.S. citizenship, and an active Secret clearance or higher.

Build Python services, APIs, and data pipelines for semiconductor manufacturing data. Develop and deploy AI/ML models and LLM solutions to automate analysis and generate actionable insights. Design data infrastructure including databases, messaging systems, and web platforms; collaborate with test, yield, and process engineers; identify automation opportunities; and maintain technical documentation.

Leads Vertex’s enterprise strategy for digital discoverability across search engines and AI-powered answer platforms. Establishes AEO, GEO, AI-ready website, metadata, schema, taxonomy, structured content, and governance standards. Partners with digital, commercial, medical, analytics, legal, privacy, agency, and technology teams to improve retrieval, indexing, citation, visibility, and measurement. Evaluates emerging AI search and conversational technologies, translates insights into recommendations, and drives organizational adoption in a complex, regulated environment.

Supports assembly, integration, testing, and final assembly of Blue Ring spacecraft. Translates engineering drawings and configurations into manufacturing processes and work instructions, resolves floor issues, manages nonconformances, reviews drawings, performs FMEA, drives DFMA improvements, and coordinates with design, test, quality, and manufacturing teams to ensure vehicle readiness and configuration control.

Deploy Vocca’s AI voice-agent product for US healthcare clients, configuring integrations, managing go-lives, and owning post-sale success and account management. Initially hands-on, the role then develops the US implementation playbook, hires and leads a deployment team, and establishes product feedback loops. The role requires autonomous startup experience, technical product configuration skills, commercial credibility with healthcare and IT stakeholders, and the ability to scale a new operations function.

Lead enterprise AI tooling strategy, governance, roadmap, and lifecycle. Maintain an enterprise inventory of AI tools, manage vendor relationships, drive tool evaluation/onboarding/retirement, ensure security and Responsible AI alignment, and enable cross-functional adoption and optimization across engineering, product, security, procurement, and governance teams.

Support and enforce enterprise data governance policies and tooling (Collibra, Ataccama). Maintain data quality, lineage, and compliance; translate steward rules into technical DQ rules; build SQL/Python solutions, dashboards, and integrations; define Jira work and collaborate with stewards, tech teams, and Data Quality squads to enable self-service governance and reporting.

Leads organization-wide AI enablement, opportunity discovery, intake, and backlog prioritization. Builds reusable AI project spaces, prompt libraries, workflow examples, training programs, and adoption feedback loops. Translates stakeholder needs into structured briefs with measurable outcomes, coordinates engineering handoffs, identifies reusable solution patterns, and communicates portfolio progress, ROI, reliability, and priorities to executives and departments.

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.

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.

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.

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.