Top AI Engineer Jobs

Design and deploy AI-native marketing workflows and agents, own end-to-end delivery from scoping to integration, evaluate frontier models and agent frameworks, build integrations into the marketing data stack, and evangelize AI adoption across marketing teams.

Build, maintain, and improve Swayable's AI/ML and scientific computing systems. Productize machine learning and data-analysis workflows, optimize high-performance distributed and parallel computing, and collaborate with engineers, data scientists, and researchers to deliver scalable, testable solutions and new product features.

Build AI-powered internal tools, prototypes, automations, and lightweight full-stack apps. Work with stakeholders to identify workflows, rapidly prototype using AI-assisted tools, design prompts and agent workflows, validate with users, iterate, document handoffs, and partner with engineering to productionize high-impact internal tooling.

Lead production agentic development and build R&D tooling that accelerates team workflows. Ship weekly in the main codebase, create and improve custom agents, MCP integrations, evals, and codegen pipelines, train engineers on new workflows, and raise team standards for prompts, tooling, and autonomous throughput.

Own and build Tern's AI and data systems end-to-end: data pipelines, production AI services, evaluation harnesses, monitoring and reporting. Ensure data quality across ingestion, modeling, and reporting, ship reliable production software, and collaborate with product squads to deliver trustworthy AI features and advisor-facing analytics.

Design, prototype, and deliver enterprise AI solutions (RAG, LLMs, agents). Architect data flows and integrations, evaluate third-party models, build production features, establish telemetry and evaluation, partner with stakeholders, maintain AI roadmap, and evangelize AI adoption across business units.

Design and implement security controls for AI systems (LLMs/NLMs/SLMs), perform red teaming and adversarial ML testing, assess AI architectures and third-party integrations, and help build RAIA standards to mitigate data leakage, model poisoning, bias, and other AI-specific risks.

Lead AI engineering on the Data Platform: build Databricks Apps deployment paths, construct an AI layer over the Medallion Architecture and semantic layer, enable LLM/NLP systems and agentic workflows, embed AI tooling in the developer lifecycle, enforce guardrails for production safety, define metadata/modeling patterns, and mentor engineers to accelerate AI adoption.

Lead software-hardware co-design to optimize LLM and agentic AI inference. Profile and optimize model execution for memory bottlenecks, influence memory and accelerator architecture, and develop/benchmark inference and serving solutions using PyTorch and vLLM while mentoring teams on best practices.

Design, build, and deploy scalable AI/ML pipelines and models for defense missions. Lead multidisciplinary teams to integrate ML into secure, classified workflows, implement DevSecOps practices, and deliver operator-facing tooling in constrained environments.

Design, build, and operate core AI platform components for training, deploying, and serving ML models at scale. Own inference workflows, GPU workload management, CI/CD for ML, observability, and operational practices. Collaborate across product, infra, security, and data teams and mentor junior engineers.

Build and integrate AI agents and automation workflows, extend full-stack apps with LLMs, manage data pipelines (Snowflake, Postgres), support server-side event tracking, analytics and dashboards, ensure secure HIPAA/SOC2-compliant handling of PII, and collaborate with leadership to deploy AI-driven marketing and compliance solutions.

Develop custom AI applications to enhance operational efficiency, partnering with multiple stakeholders, and maintaining internal capabilities while ensuring data security and compliance.

Work directly with clients to design, build, and deploy end-to-end production software and AI solutions. Lead solution architecture across backend, frontend, data pipelines, and ML models; develop production-grade applications; engage stakeholders from engineers to executives; and contribute reusable frameworks and best practices.

Design, build, and maintain production-grade AI Assistants and backend services integrating LLMs with enterprise tools. Implement prompt engineering frameworks, evaluation pipelines, APIs, and observability. Collaborate with AI Ops, security, IT, and platform teams to ensure reliable, secure, and scalable deployments and create repeatable frameworks, documentation, and onboarding materials.

Lead QA/SDET efforts for an enterprise data and analytics platform: design and implement automated data validation and data quality frameworks across Medallion layers, integrate tests into CI/CD, perform cross-source reconciliation and UAT for BI tools, drive defect triage, and ensure lineage, auditability, and model testing from feature engineering through scoring.

Embed with internal teams to map workflows, design and ship production agentic AI systems that automate CX, finance, ops, and developer productivity. Build LLM integrations, observability, APIs, reusable automation modules, and data products to replace manual operational work.

Early-career role to assist design, develop, and deploy AI-enabled applications and cloud-native solutions on AWS and Azure. Work includes prompt engineering, RAG workflows, infrastructure-as-code, containerized deployments, CI/CD, and integration with enterprise systems while supporting compliance with NIST AI RMF and FedRAMP.

Design and implement AI-powered sales automation, GTM systems, and revenue intelligence. Build integrations, workflows, enrichment, segmentation, dashboards, and autonomous pipelines to prioritize accounts, improve forecasting, and automate RevOps processes while enabling Sales adoption and cross-functional alignment.

Design, build, and operate AI-powered sales automation and RevOps systems. Own GTM technology stack, integrations, and workflows to prioritize accounts, enrich and segment data, automate outreach and lifecycle processes, and improve forecasting and pipeline visibility. Partner with Sales leaders to deploy automation, enable adoption, and deliver revenue intelligence and operational dashboards that drive account prioritization and improved GTM performance.

Build and scale AI data and ML infrastructure: evaluation systems, fine-tuning pipelines, agent-first product surfaces, high-throughput data workflows, and integrations of new models into production.

Lead architecture and implementation of AI/ML production systems for Growth: APIs, data pipelines, evaluation and monitoring. Partner with ML/research teams to productize models, build cross-team foundations (eval, content quality, agent platforms), manage latency/cost/quality tradeoffs, ship at scale, and mentor engineers as a peer Staff IC.

Design, build, and operate agentic AI systems for Roku TV: prototype, architect, and deploy agents and copilots; implement RAG, tool/function-calling, orchestration, observability, evaluation, and safeguards; integrate agents with backend services and production pipelines; collaborate across teams to deliver scalable, reliable solutions.

Lead AI operations engineering to design, build, and scale OCI-based GenAI infrastructure and MLOps pipelines. Architect hybrid GPU deployments, automate training-to-inference workflows, deploy multimodal LLM services, ensure HIPAA-compliant AI systems, drive OCI-focused R&D, and mentor junior engineers.

Build and deploy agentic, multi-step AI workflows and reusable agent skills; generate production AI images; use AI coding tools daily; develop API integrations (Google Workspace, Notion, Slack, Workable); own projects end-to-end including logging, error handling, and monitoring.