Top Artificial Intelligence Jobs

Support AI, analytics, and data modernization for VA research: perform ETL, statistical programming, reporting, dashboards, LLM-enabled analytics, data integration, quality validation, and executive-level presentations to improve operational decision-making.

Lead product marketing for Zendesk's AI platform, driving launch, adoption, and technical messaging. Own GTM strategy, sales enablement, release marketing, customer storytelling, and cross-functional alignment with Product, Sales, and Marketing to increase adoption and impact.

Design, develop, and deploy AI/ML models and production systems. Collaborate with cross-functional teams, mentor engineers, and ensure model performance, scalability, and reliability. Drive technical strategy for AI solutions.

Lead and build Inflection's realtime Voice AI stack across speech models, streaming systems, voice-agent runtime, and evaluation. Define roadmap, direct research and engineering, design evaluation metrics beyond WER, deploy enterprise voice agents, debug production behavior, and mentor a team focused on speech research, realtime systems, and audio infrastructure.

Lead architecture and implementation of production agentic AI systems: build agents that plan, retrieve context, call tools, and execute workflows; design architectures for memory, tool routing, state, safety, and human approvals; create evals and observability for multi-step tasks; integrate with APIs and enterprise systems; improve reliability and guardrails; choose models and infrastructure; mentor senior engineers and own technical direction.

Lead design, deployment, and maintenance of large-scale Generative AI solutions (RAG and Agentic AI). Architect secure document processing pipelines, integrate responsible AI practices (toxicity/bias screening), implement data security and compliance, monitor AI infrastructure, and collaborate with cross-functional governance and legal teams.

Architect and operate a global AWS-based Generative AI/LLM platform: design scalable GenAI infrastructure, implement IaC and CI/CD (CDK, Projen, Argo CD), manage k8s/ECS workflows, establish governance, perform cost/performance analyses, and support ML/DS lifecycle in a regulated life-sciences environment.

Design, deploy, and operate production-grade Generative AI agents and GenAI workflows for clinical trial optimization. Build cloud-native infrastructure, CI/CD, monitoring, evaluation, and scaling using AWS, containerization, CDK, and LLM frameworks. Collaborate with AI engineers and data scientists to transition research to reliable, explainable, and cost-efficient production systems.

Lead SEO strategy and execution focusing on AI-driven and traditional search visibility. Optimize content for AI answer engines (GEO), run technical audits, build AI/automation workflows for keyword research and reporting, and translate SEO performance into client-ready insights while collaborating with developers and cross-functional teams.

Design, build, and deploy production GenAI solutions (RAG, agentic systems, document intelligence, copilots) for multifamily workflows. Lead LLM application technical design, prompt engineering, model selection, evaluation, guardrails, and responsible AI practices. Partner with product, data science, and business stakeholders to deliver measurable, compliant AI-driven outcomes.

Lead development of an agentic AI platform: build agent orchestration, RAG/CAG pipelines, embeddings and vector-store ops, integrate LLMs (Anthropic Claude primary), deliver full-stack features across Python/Node/React, own production deployments, evaluation pipelines, and mentor a small senior engineering team while working hybrid in Denver.

Lead and grow a hands-on AI Products engineering team building LLM and agent capabilities. Own technical vision, evaluation practices, and production delivery. Split time between management and coding, drive 0-to-1 product work, and ensure model quality, reliability, and upgrade pipelines.

Partner with teams to design, pilot, and scale generative-AI workflows and automation. Build prompt libraries, evaluate AI platforms, integrate APIs, draft executive communications, and drive cross-functional adoption of AI solutions to improve efficiency and decision-making.

Design, develop, and deploy ML and deep-learning solutions (including generative AI). Lead end-to-end model lifecycle, build RAG/agentic workflows, integrate models into production, collaborate with engineering and clients, monitor performance, and document experiments and data provenance.

Own strategy, design, and optimization of Vanta's customer-facing chatbot and internal AI Copilot. Build and maintain AI-driven knowledge, design conversation flows in Intercom, analyze performance data, and collaborate cross-functionally to increase resolution rates and CSAT. Handle a small volume of support tickets to inform improvements and maintain product expertise.

Lead and manage an onsite Edge AI Applications Lab to design, fabricate, and deploy tiny-ML models on microcontrollers and embedded platforms. Oversee lab projects, hardware prototypes, sensor data collection, model development, and cross-functional/customer collaborations. Mentor junior engineers, manage procurement and scheduling, and optimize real-time edge solutions for consumer, industrial, and automotive applications.

Founding pre-sales Field Engineering role selling STEM datasets, benchmarks, graders, and expert services to frontier AI labs. Lead technical discovery, design solution architectures, build prototypes/demos, run POCs to conversion, interface between GTM and R&D, and codify playbooks and demo libraries to scale STEM opportunities.

Lead and deliver GenAI projects: design and architect multi-agent LLM systems, build training/fine-tuning and deployment pipelines (RAG), own technical roadmaps, mentor engineers, implement evaluation and observability pipelines, and integrate solutions on cloud platforms to meet enterprise requirements.

Own and optimize on-board inference architecture for humanoid robots: map and partition models across heterogeneous accelerators, enforce compute budgets, optimize toolchains and kernels, apply quantization/pruning, profile pipelines, and collaborate with AI, platform, and silicon vendors to meet real-time power, latency, and reliability constraints.

Lead hands-on design and build of secure AI/ML solutions for federal agencies: engineer LLM orchestrations, RAG pipelines, vector DBs, hybrid cloud/on-prem integrations, DevSecOps, compliance (FedRAMP/NIST), ATO support, and executive technical advisement.

Design, build, and optimize AI-driven customer success automations using no-code/low-code tools and LLM APIs. Identify use cases across the customer lifecycle, integrate solutions with enterprise platforms, apply prompt engineering and RAG patterns, monitor performance, and collaborate cross-functionally to scale AI-enabled workflows while maintaining responsible AI practices.

Develop and advance state-of-the-art deep learning methods for protein and antibody design. Lead projects on generative and foundation models, implement large-scale distributed training and GPU-based inference, collaborate with biology and drug discovery experts, publish and communicate research, and contribute to open-source efforts.

Senior technical leader responsible for the success of complex enterprise AI deployments: setting architecture and evaluation strategy, ensuring production reliability, coaching and developing engineering leads, partnering with customer executives, debugging critical issues, and scaling pilots to enterprise deployments while using AI-native engineering practices.

Design, develop, and deploy end-to-end ML/AI solutions (RAG, retrieval, embeddings, knowledge graphs) on cloud platforms. Build ingestion pipelines, implement secure self-hosted models, ensure PHI/PII compliance, advise clients, mentor team members, and deliver measurable outcomes.

Design, build, deploy, and operate production AI systems for customers. Implement end-to-end AI workflows (models, prompts, agents, retrieval, evaluators), write maintainable Python services, monitor and debug live systems, create evaluation and observability tooling, and work closely with stakeholders to iterate systems for reliability and business value.