Top Artificial Intelligence Jobs

Designs, deploys, and supports production AI/ML solutions across data ingestion, feature engineering, model training, MLOps, monitoring, and lifecycle management. Builds AWS cloud-native applications, generative AI systems, knowledge graphs, and real-time or batch pipelines. Partners with stakeholders on analytical solutions, ensures data quality and AI governance, leads technical design, and mentors engineers.

Own the architecture and development of data-intensive web applications powering customer insights. Provide hands-on technical leadership, mentor engineers, conduct architecture and code reviews, resolve complex distributed-system issues, and deliver performant, maintainable data products. The role involves ETL and/or RAG development, full-stack work across React, TypeScript, Next.js, PostgreSQL, and C#/.NET, with opportunities to operate cloud infrastructure and observability systems.

Develop and deploy production-ready AI models, agentic applications, and RAG systems for investment and business use cases. Build data pipelines, optimize AI performance, and manage cloud-based deployments. Partner with stakeholders to gather requirements, shape AI product roadmaps, and deliver measurable value. Research emerging AI methods, establish best practices, mentor junior team members, and lead internal training.

Supports the design, implementation, optimization, testing, deployment, and ongoing support of AI-enabled applications and automation solutions in a healthcare environment. The role analyzes workflows, identifies improvement opportunities, evaluates emerging AI tools, troubleshoots application issues, monitors performance, and partners with clinical, operational, and IT stakeholders to drive adoption and improve patient care.

Partner with enterprise customers to identify AI use cases, architect and implement custom applications and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into actionable solutions, collaborate with Product and Engineering, improve technical integration processes, and drive customer adoption, time-to-value, and measurable business outcomes.

Partner with enterprise customers to identify AI use cases, architect and implement custom applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into solution recommendations, collaborate with Product and Engineering, develop scalable integration processes, and drive customer adoption, time-to-value, and business outcomes.

Lead independent AI research on large language models and agentic systems, from hypothesis through training, evaluation, and production deployment. Develop post-training methods, reasoning and planning capabilities, evaluation benchmarks, synthetic data pipelines, and enterprise AI improvements. Publish at leading conferences, shape model strategy, and mentor researchers and engineers.

Build and deploy scalable AI applications, intelligent agents, and generative AI features in production. Collaborate with research, data science, and product teams; evaluate emerging AI technologies; and implement responsible AI practices including bias detection, explainability, and safety alignment. The role requires production machine learning experience, Python and AI framework expertise, cloud and MLOps knowledge, and strong software engineering skills.

Partner with enterprise customers to identify AI use cases, validate feasibility, and implement tailored solutions using WRITER’s platform, APIs, and Knowledge Graph. Architect applications, templates, and integrations; explain AI capabilities to technical and executive audiences; collaborate with Product and Engineering; develop scalable integration processes and documentation; and drive customer adoption, expansion, and measurable business outcomes.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows. Own architecture from proposal through production, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement deployment, monitoring, logging, enterprise security, and reliability capabilities while mentoring engineers and delivering resilient production systems.

Develop and deploy large-scale computer vision and machine learning models for Coupang’s search and product experiences. Responsibilities include experimentation, statistical analysis, scalable model development and serving, A/B testing, researching advanced vision techniques, presenting results to leadership, and mentoring engineers. The role requires applying deep learning, transformers, generative modeling, and other state-of-the-art approaches to user-facing products.

Design and operationalize AI/ML capabilities for secure DoD environments, including agentic workflows, production APIs, MLOps, model deployment, monitoring, validation, and continuous improvement. Integrate structured and unstructured data with Foundry applications and mission systems. Collaborate with engineering and mission teams, troubleshoot production systems, and apply responsible AI, security, governance, and model-management practices.

Designs, develops, deploys, and supports generative AI applications, APIs, workflows, and reusable components. Evaluates foundation models and deployment approaches, integrates RAG, prompting, tool use, and agentic patterns, and establishes testing, guardrails, documentation, standards, and observability practices. Advises project teams on architecture, security, GPU capacity, costs, performance, privacy, and responsible AI. Collaborates with engineering, infrastructure, security, operations, and business stakeholders to enable scalable enterprise AI adoption.

Owns the post-release lifecycle of multiple agentic AI products, including backlog management, prioritization, release planning, and continuous optimization. Translates stakeholder feedback, usage patterns, defects, and AI performance insights into product requirements and prioritized work. Coordinates business stakeholders, engineering, QA, AI Enablement, and AI Trainers while making product trade-off decisions and improving scalable operating practices for live AI products.

Architect the infrastructure powering Pinterest’s Generative AI and recommender systems at petabyte scale. Set the AI Platform’s technical vision and roadmap, lead cross-functional initiatives spanning data orchestration, model training, fine-tuning, evaluation, feature stores, and high-performance inference, and drive company-wide architecture decisions. Foster an inclusive engineering culture while maintaining high standards for quality, ownership, and accountability.

Defines enterprise MLOps, DevOps, cloud, and hybrid AI architecture for Wind Engineering. Establishes production-grade CI/CD and continuous training pipelines, model lifecycle standards, observability, reliability, drift management, security, governance, and auditability practices. Reviews AI designs for scalability and supportability, leads technical transitions into sustainable GE Vernova ownership, resolves escalations, and mentors architects and engineering teams.

Leads AI security, platform modernization, and developer productivity initiatives. Designs enterprise solutions, manages AI vulnerability remediation, develops CI/CD and DevSecOps automation, modernizes Java platforms, enables rapid security releases, and establishes secure AI development standards. Partners with architecture, security, infrastructure, and engineering teams to deliver secure, scalable platforms and improve software delivery efficiency.

Owns quota and drives adoption of Zendesk AI products across existing and new accounts. Partners with account executives to prospect, demonstrate, position, and close AI opportunities; leads proof-of-concepts, supports commercial modeling, identifies expansion opportunities, and enables sales teams. Requires strong AI product knowledge, value-based selling skills, customer engagement expertise, and experience exceeding sales targets.

PhD summer intern developing AI agents with LLMs, deploying deep learning inference models through Python and FastAPI, integrating AI into R&D workflows, processing datasets, and delivering AI-driven insights. The role requires collaboration with R&D teams and is a full-time, 12-week, in-office internship in Mason, Ohio.

Owns end-to-end release management for the C3 Agentic AI Platform, including scope, scheduling, readiness criteria, risk management, go/no-go decisions, deployment, rollback, incident resolution, communications, tooling, metrics, retrospectives, and process improvements. The role partners with engineering, product, data science, quality, infrastructure, and cloud operations teams to deliver reliable enterprise AI platform releases.

Leads end-to-end production support for enterprise AI, intelligent automation, and RPA programs. Administers UiPath Orchestrator, manages automation deployments, monitors jobs and logs, and oversees L1/L2 support vendors. Owns escalations, vendor SLAs, production readiness reviews, staff mentoring, stakeholder communication, and continuous improvement. The role ensures automation stability, identifies design risks, and translates technical issues into business impacts.

Architect and build production agentic AI systems for customer support and compliance automation. Lead technical direction for distributed systems, APIs, orchestration frameworks, guardrails, and evaluation systems. Create reusable automation primitives, improve grounding and reliability, reduce hallucinations, and mentor engineers on LLM application design and production readiness. Collaborate cross-functionally to scale AI automation across Coinbase.

Build backend systems that translate computer vision and machine learning outputs into interactive product experiences. Responsibilities include designing perception-to-product data contracts, shipping AI-generated geometry to a canvas, balancing accuracy with user experience, closing human correction loops, and bridging AI/ML and product engineering teams. The role requires Python and TypeScript, familiarity with PyTorch and model postprocessing, data contract versioning, and experience deploying computer vision or ML outputs in user-facing products.

Designs, builds, and ships AI-enabled mobile experiences for frontline and field teams using Tulip. The role digitizes manual workflows, supports offline-first capabilities, integrates enterprise and AI services, and contributes to platform architecture, security, testing, observability, and release practices. The engineer conducts field discovery, partners with product and design teams, mentors peers, and delivers secure, accessible, high-performing applications aligned with measurable business outcomes.

Leads an AI engineering squad, managing performance, mentorship, career development, technical architecture, and delivery. Partners with Product on technical roadmaps, metrics, and scalable solutions; guides system design, AI-assisted development, documentation, and process improvements. Requires strong software engineering and AI agent experience, technical leadership, stakeholder collaboration, and a delivery-focused mindset.