Top Artificial Intelligence Jobs

Lead AI product strategy for investor workflows, building AI-native products (agents, copilots, MCPs, APIs) that turn proprietary data into reliable, production-grade investor tools. Partner with customers, engineering, design, and research to run experiments, define quality/trust frameworks, and ship 0→1 products that automate and augment investment decision-making.

Design, build, and operationalize scalable AI/ML infrastructure and data pipelines. Implement model lifecycle management (MCP), integrate agent frameworks (e.g., LangChain), manage vector DBs and feature stores, enable HITL workflows, maintain data architectures, mentor junior engineers, and present work internally and externally.

Design, build, and scale AI-powered operational platforms integrating LLMs, monitoring, CI/CD, ticketing, and cloud services. Develop APIs, data pipelines, front-end apps (TypeScript/React/Next.js), backend systems, evaluation/observability tools, and reusable frameworks. Collaborate across SRE, Product, and Infrastructure to deliver production-grade, automated AI workflows and improve system reliability and performance.

Lead and build Console's enterprise customer enablement function end-to-end: onboarding, training, documentation, self-serve resources, and metrics. Partner with Product, Engineering, Sales, and Forward Deployed Engineering to design scalable learning experiences, measure effectiveness, and iterate to drive adoption and retention for enterprise AI agents.

Design, build, and maintain scalable full-stack systems for underwriting experience, modernizing customer correspondence and automating manual processes using AI. Collaborate cross-functionally, participate in design and code reviews, define product/architecture, and apply DevOps and cloud practices to deliver production-ready solutions.

Build and maintain backend, CI/CD, and data integrations for the NOS platform. Ramp on Palantir Foundry and AI/data tooling, write production code and tests, and collaborate with engineers and data scientists under senior review to deliver scoped features.

Customer-facing specialist who leads product demos, conducts workflow discovery, tailors use-case presentations, and collaborates with Sales, Product, and Engineering to drive AI adoption in fashion design, merchandising, and product development.

Design and evaluate AI-enhanced instructional materials, lead Agentic AI implementations for adaptive learning, collaborate with faculty and technologists, run needs analyses and effectiveness evaluations, and deliver faculty training on AI in education.

Develop, test, and maintain AI-powered applications using generative AI and LLMs; collaborate with cross-functional teams to implement AI solutions, analyze data to inform features, and optimize application performance.

Serve as a senior, client-facing advisor guiding enterprise customers on AI-driven marketing strategy, adoption, and optimization. Own strategic relationships, deliver executive insights and QBRs, translate data into action, codify best practices, and collaborate cross-functionally to align product and customer outcomes.

Join a remote AI consulting team to develop AI agents, work with LLMs and enterprise data, document SOPs and processes, drive AI adoption, and collaborate on scalable AI solutions while continuously learning.

Design, test, and maintain reusable prompt templates for enterprise LLM use cases. Run A/B tests, collaborate with trainers and integrators to embed prompts into RAG and AI workflows, and keep prompt libraries updated with best practices and client guidelines.

Lead design and deployment of AI solutions (copilots, agents, RAG) into client workflows: analyze processes, coordinate pilots, integrate prompt libraries and APIs, support change management, track adoption, and iterate post-launch.

Lead client engagements from kickoff to delivery, translate business challenges into actionable AI transformation plans, manage stakeholder relationships (including C-suite), create polished client deliverables, coordinate with engineering teams, track multiple projects, mitigate risks, and refine internal processes to scale delivery quality and speed.

Design and build full‑stack data capture workflows (Next.js web, Python desktop), architect high‑throughput data pipelines with low latency, collaborate on product strategy, ship features rapidly, and establish engineering best practices for team growth.

Lead and manage a small engineering team while remaining hands-on: build and ship scalable production systems for applied AI, drive execution, recruit and mentor engineers, and collaborate across ops, domain experts, and customers.

Audit and monitor data labeling outputs, provide actionable feedback, support onboarding and training of annotators, enforce and improve guidelines, escalate workflow issues, and document findings to maintain high-quality human-in-the-loop AI data processes.

Lead client-facing presales and enterprise architecture for data modernization and AI adoption. Design cloud-native data platforms and advanced AI/ML solutions (Agentic AI, RAG, vector DBs), provide hands-on technical execution, guide global delivery teams, ensure governance and secure integrations across Azure/AWS/GCP, and contribute to internal R&D and presales strategy.

Lead backend architecture and system design for an AI-driven manufacturing planning platform. Build and scale a Node.js/Python stack with Azure Databricks, implement optimization and scheduling algorithms, establish engineering patterns, and mentor/lead a small early-stage engineering team while collaborating with product and data functions.

Design and implement algorithms for packing and allocation in logistics, ensuring operational feasibility. Manage data and integrate solutions into workflows.

The AI Software Engineer will develop and deploy AI-powered solutions, build MLOps pipelines, integrate data sources, and ensure model performance in a logistics context.

As an AI Engineer, you will design and implement systems that enforce feasibility and authority in logistics decision-making, ensuring compliance with command policies. You will work on integrating AI reasoning with execution workflows, handling data validation, and improving the decision-making framework.

Citizen Health seeks AI/Machine Learning Engineers to develop AI solutions for healthcare using advanced ML techniques, focusing on rare diseases and patient care. Responsibilities include designing ML models, optimizing LLMs, and ensuring compliance.

The AI Engineer will build AI and computer vision systems for food waste management, focusing on model training, evaluation, and product feature integration.

The Software Engineer AI will design, develop, and deploy AI-powered product features in a cloud-native environment, ensuring technical excellence and collaboration across teams to enhance the candidate experience.