Top Artificial Intelligence Jobs

Lead the Brex Assistant product end-to-end: set strategy and roadmap, define agent capabilities, design prompts and evals, prototype and iterate on LLM/agent behavior, analyze failures and usage, and partner with engineering, design, and research to deliver a trustworthy, action-taking conversational AI in a financial context.

Design, build, and operate production LLM-driven agents that crawl, extract, and verify web evidence to enrich business identities. Own classification, discovery, linkage, risk scoring, shared agent infrastructure, model/tool selection, prompt engineering, eval methodology, and cost/latency optimizations for real-time enrichment pipelines.

Design, build, and maintain internal AI APIs, SDKs, and developer tooling to abstract LLM complexity. Implement CI/CD-integrated AI systems, test-generation and evaluation pipelines, observability for model behavior and costs, shared prompt libraries, model routing, and context management. Integrate AI into workflows (Jira, GitHub, Slack), enforce engineering standards, and provide architectural guidance to product teams.

Lead training, adaptation, evaluation, and productionization of Lila's AI models for scientific customer workflows. Build evaluation loops, design experiments, debug model failures, integrate model improvements into product workflows, and create reusable tooling for model adaptation, evaluation, and deployment while collaborating across research and software teams.

Lead the AI product vision and roadmap across corporate solutions, translating LLMs and ML into SaaS features. Partner with product, engineering, and data science teams to prioritize, develop, pilot, and commercialize AI-native and AI-enabled capabilities for governance, compensation, sustainability, and cyber risk.

Design, build, and operate production-grade AI systems (agents, RAG pipelines, LLM workflows). Translate product needs into technical designs, run offline/online evaluations, optimize inference and cost, implement observability and guardrails, and mentor teams to deliver reliable, responsible AI capabilities.

Create hands-on, on-camera educational content teaching AI tools and workflows. Test and evaluate new AI tools, script and record lessons, host live sessions and office hours, provide personalised feedback, and collaborate to build courses, guides, and workshops for a large AI community.

Lead product and service design for applied AI solutions. Define product strategy and roadmaps, design GenAI and conversational interfaces, build prototypes and design systems, run user research and usability testing, collaborate with engineering on front-end integration, and establish reusable frameworks and accessibility standards to accelerate platform team delivery.

Build LLM-powered full-stack features: vector-backed expert and solicitation indexes, search (OpenSearch/PGVector), APIs and UI. Ship production-quality Python/Go and React code, using AI tools daily, ensure security, reliability, and scalability while iterating rapidly on an AI-first platform.

Build and deploy AI-powered features and full-stack applications for healthcare platforms using LLMs and modern AI engineering tools. Develop APIs, backend services, automation pipelines, and production-ready MLOps/DevOps workflows. Collaborate with product, design, and clinical stakeholders, research emerging AI technologies, and support scalable, secure deployments in an Agile remote environment.

Design and build AI solutions using Microsoft AI stack (Azure, Copilot Studio, Azure AI Foundry). Develop copilots, RAG and agentic workflows; create API services, implement secure data access, and build CI/CD pipelines. Collaborate with product, engineering, security, and business teams to deliver compliant, observable, and scalable AI capabilities for utilities.

Designs, develops, tests, deploys, and maintains payroll and AI-driven software solutions. Collaborates in agile teams, mentors junior engineers, builds test suites, improves performance, security, integrations, and operational monitoring while driving process improvements through automation and emerging technologies.

Lead change management and adoption for AI engagements: develop change strategies, stakeholder engagement, communications, training, and adoption metrics. Partner with leaders and project teams, support transitions through implementation, monitor outcomes, and recommend policy and process improvements. Support Agile and Traditional SDLC efforts and use collaboration tools to drive sustained user adoption.

Lead development and oversight of AI governance frameworks, policies, controls, and risk/compliance practices. Partner with business, delivery, and technical teams to align governance with project delivery, support SDLC-based project governance, recommend improvements, and communicate governance expectations across the organization.

Lead project and portfolio management for AI initiatives, overseeing planning, execution, governance, risk management, reporting, and continuous improvement. Coordinate stakeholders and technical teams, apply Traditional and Agile SDLC, maintain PMO standards, supervise team members, and communicate status and recommendations to executives.

Lead a core AI agent product area for healthcare revenue cycle automation. Define strategy, metrics, and roadmaps; partner with Engineering, Operations, and customers to improve agent reliability, workflow completion, and scalability. Use data, pilots, and feedback to iterate, define quality measures, support GTM, and build repeatable product patterns for scaled deployments.

Lead development and deployment of AI-first systems: build generative and discriminative models, enable multimodal behavior classification from mobile signals, define prompting and benchmarking standards, own ML tooling and SDLC, and partner with Product and Engineering on the AI roadmap.

Lead design and delivery of enterprise AI solution architectures and roadmaps. Manage a portfolio of AI initiatives, advise executive leadership, and build/deploy advanced systems (LLMs, generative AI, agents, RL, computer vision). Establish secure, compliant MLOps and CI/CD pipelines for regulated environments, transform R&D into governed offerings, mentor teams, and prototype solutions for stakeholder adoption.

Lead enterprise AI strategy and execution: identify high-impact use cases, build business cases and ROI, manage cross-functional teams to deliver MLOps-powered solutions, ensure responsible AI governance, and present results to executives to scale automation and operational efficiency across functions.

Lead enterprise AI architecture for GenAI, agentic workflows, ML platforms, and data ecosystems. Define scalable AI solution patterns, evaluate and integrate emerging tooling, enforce governance and security, mentor engineering teams, and align architecture with business objectives. Collaborate with cross-functional stakeholders to embed AI into end-to-end SaaS solutions and drive technical strategy and delivery.

Lead enterprise data and AI strategy and build scalable data infrastructure for a biotech company. Deliver prioritized AI use cases, enable productivity tools, ensure regulated data and AI controls, and partner cross-functionally to create reporting, governance, and analytics that support clinical, regulatory, medical affairs, commercial, and executive decisions.

Work with enterprise customers to design, prototype, and deploy production-ready AI agents using IFS Loops. Integrate with customer systems, tune agent behavior, troubleshoot deployments, and hand off stable solutions while collaborating with Product, Engineering, and Customer Success.

The Senior Data Engineer designs AWS-native data foundations for AI applications, focusing on knowledge graphs, data quality, and data governance. Responsibilities include data modeling, pipeline building, and mentoring other engineers. The role requires collaboration across teams to integrate data access with security policies and improve AI system effectiveness.

Lead enterprise AI adoption by assessing processes, identifying AI use cases, developing implementation playbooks, and running workshops. Partner with IT, Security, Compliance, HR, and business units to design AI-enabled workflows, ensure governance and regulatory alignment, deliver role-based AI education, and measure adoption and productivity gains while preserving human oversight and organizational knowledge.

Design, prototype and deliver full-stack banking solutions using Python/NodeJS/ReactJS. Build ML/GenAI and LLM-based features, APIs (OpenAI standards), reliable production services, databases (SQL/NoSQL), CI/CD pipelines, and provide 3rd-line support.