Top Artificial Intelligence Jobs

Design and prototype LLM- and agent-based workflows (RAG, tool-calling), build reusable connectors and agent templates, integrate data science models, define clean API boundaries, implement lightweight evaluation/logging, and collaborate with data science and leadership to operationalize AI systems.

Design, deploy, and operate production AI systems for enterprise customers. Build compound workflows combining models, agents, retrieval, and evaluation. Measure and iterate on live systems, integrate with customer data and APIs, and collaborate with stakeholders to ensure reliability and business impact.

Design, build, and operate end-to-end AI systems in enterprise environments. Own architecture, integrations, security, observability, and operational patterns; deliver reference implementations and resolve high-impact production issues while partnering closely with customer engineering and security teams.

Design, build, and own the end-to-end model platform: training/fine-tuning pipelines, RL workflows, data ingestion/curation, reproducible experiments, scalable inference services, and production deployment.

Lead AI integration and full-stack development across the platform, rapidly prototype and ship production-quality features using Node.js, React, TypeScript, Ruby, GraphQL and Postgres, ensure secure handling of healthcare data, set technical standards, and mentor engineering team members.

Design, build, and deploy generative AI applications and agentic workflows using LLMs. Develop orchestration, tool-use, memory, and SDKs; partner with stakeholders to create AI copilots; ensure scalability, governance, and security with platform and MLOps teams.

Build, deploy, and productionize generative AI applications and agentic systems, including CI/CD pipelines, monitoring, and governance. Integrate AI solutions with enterprise systems, prototype and scale copilots and multi-agent workflows, and optimize pipelines for performance, cost, and scalability.

Lead design and implementation of defensible Generative AI and ML solutions for eDiscovery. Consult with clients, optimize models, ensure AI auditability/QC, train internal teams, and support KPI reporting across litigation workflows.

Apply AI/ML and data-driven techniques to accelerate NAND flash controller design verification. Build models and tooling for failure triage, anomaly detection, and root-cause analysis, integrate ML solutions with verification pipelines, and collaborate with design, DV, firmware, and infrastructure teams to improve debug efficiency and coverage closure.

Develop production-ready code in Python and TypeScript for AI applications, optimize AI performance, integrate enterprise data, and ensure security through testing.

Lead Thermo Fisher's enterprise AI-driven search and discoverability strategy. Manage multi-disciplinary teams, set governance and standards for generative search, oversee technical search health, direct AI-ready content, build visibility monitoring and reporting, partner across Product, Digital, IT, Analytics, and Media, and manage external vendors to improve visibility and accuracy across search and LLM-driven experiences.

Design, build, and implement conversational AI solutions (IVR, speech analytics, quality management). Gather requirements, perform no-code development, analyze performance metrics, document processes, enable partners, and recommend process improvements to drive scalable customer-focused solutions.

Lead technical strategy and delivery for a 12-month AI Platform SRE transformation: align executives, define SRE operating model, guide architecture and tooling, track reliability and automation metrics, and oversee phased vendor-to-client transitions.

Lead and grow the AI Platform team to deliver shared AI/ML services, infrastructure, and tooling. Define platform vision with the architect, enforce standards for reliability, security, and observability, and collaborate with cross-functional teams to enable secure, self‑service developer capabilities.

Build production-ready intelligent automations and agentic AI solutions. Full-stack development using TypeScript or Python, LangChain, vector DBs, RAG, and tool-calling. Collaborate with clients, ensure maintainable, tested, scalable deployments, CI/CD, observability, and model fine-tuning or deployment.

Manage AI intake and triage via ServiceNow, support AI risk/impact screenings and inventory, develop change management and training materials, monitor operational metrics, and collaborate with legal, data governance, and technology stakeholders to operationalize AI governance.

Lead AI & Commerce Foundations for a B2B e-commerce platform: own platform architecture (auth, APIs, payments, loyalty), define LLM integration strategy, prioritize partner-facing features, run zero-to-one delivery, collaborate with engineers and data scientists, and use data and partner feedback to drive product decisions and rapid shipping.

Lead the AI & Commerce Foundations team to define AI strategy and own core platform architecture (auth, APIs, loyalty, payments). Integrate LLMs to create partner value, prioritize and ship partner-facing B2B features, run experiments, and collaborate with engineers, data scientists, and partner success to deliver MVPs and iterate based on data and customer feedback.

Lead end-to-end client engagements to design, build, and ship AI agent workflows and automations. Convert ambiguous AI mandates into sequenced initiatives, prototype and deploy solutions, manage stakeholder change, document learnings to inform product strategy, and help build the operating model for this new function.

Owner of AI-driven automation and reporting for Finance. Design and deploy AI workflows, scripting, and dashboards to automate revenue, billing, AR, cash flow, and reconciliations across multiple data sources while ensuring controls, auditability, and cross-functional alignment.

The Customer Onboarding Specialist will drive the onboarding process for new customers, ensuring rapid adoption and value from EvenUp's platform while coordinating with various teams and training users.

Design, prototype, and ship AI-powered features (copilots, agents, RAG, extraction, routing) end-to-end. Collaborate with Product, Design, Data, and customers; produce production-grade code, tests, and documentation; iterate quickly using modern AI tooling and observability.

Lead engineering-embedded delivery across global teams by translating product intent into testable work, building automated validation, CI/CD and flow-metrics infrastructure, deploying forecasting/risk models, automating coordination, writing production code to solve delivery problems, designing multi-agent AI workflows, and driving adoption of DevOps/MLOps governance.

Lead and grow a team of autonomy perception TPMs, set program direction, translate strategy into roadmaps, manage schedules, risks, budgets, and stakeholder communications across autonomy software teams.

Lead and deliver enterprise AI-ready data platform transformations: design cloud-native architectures, prototype AI-assisted pipelines, embed governance and compliance, mentor teams, and partner with executives to modernize data systems for real-time analytics and AI use cases.