Top AI Engineer Jobs

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.

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.

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.

Design and build scalable backend services and data ingestion pipelines to process and enrich data (including LLM enrichment). Perform data modeling, mapping, and large-scale file manipulation while collaborating in an Agile team to deliver reusable APIs, optimize performance, and improve processes.

Senior DevOps engineer building AI-powered agents, automation pipelines, and full‑stack business applications. Design and maintain integrations (SAP, Salesforce, Google Workspace, Slack, Jira), REST APIs, CI/CD, and containerized deployments. Lead AI standards, prompt engineering, agent frameworks, cross-functional discovery, documentation, and enablement while helping grow an AI Automation & Integrations practice.

Provide deep technical support for Devin and Windsurf by reproducing, diagnosing, and resolving complex customer issues across codebases, CI/CD, containers, cloud platforms, and developer tooling. Perform root-cause analysis, escalate with full technical context, create playbooks and tooling, and support release testing to improve reliability and adoption.

Design, build, and ship enterprise-grade AI agents and LLM-backed workflows for legal workflows. Implement retrieval, memory, evaluation, benchmarking, and feedback loops; work with production systems, data collection, and cross-functional teams to improve platform performance and compliance.

Lead QA for Data & AI features: design and run test strategies for ML/LLM outputs, write data validation in Python/SQL, implement Databricks pipeline checks, automate tests (Katalon/Playwright), validate APIs/ETL, monitor model performance and data drift, and collaborate across Engineering, Product, and Data to define acceptance thresholds and CI/CD quality gates.

The role involves designing and deploying AI/ML models, collaborating with stakeholders, and enhancing operational efficiency through innovative solutions.

Lead cross-functional engineering workstreams to design software, integration, data, testing, and infrastructure architectures. Define and encode engineering best practices into AI coding agents, design AI-assisted workflows, select AI tools, implement CI/CD, automated and chaos testing, and lead performance, integration, and data strategies. Provide technical leadership, mentorship, and measurable quality reporting at enterprise scale.

Build and operate integrations and automations using iPaaS and APIs, ship production AI/LLM agents and workflows, implement identity-driven provisioning (SSO/SCIM/OAuth), and deliver monitored, reliable automations with runbooks while partnering across teams to scale systems.

Lead developer-focused growth for DeepL's headless surfaces (MCP, CLI). Drive discoverability, instrument activation funnel to paid usage, create production-quality developer artifacts and content, build ecosystem presence, and feed actionable developer insights into product and engineering.

Design, develop, test, and maintain cloud-native, API-driven AI/ML software solutions. Lead technical delivery, design scalable architectures, implement AWS-based deployments, productionize ML/GenAI applications, and collaborate with stakeholders to translate business needs into secure, maintainable systems.

Design, build, and maintain end-to-end agentic intelligence systems that ingest social, on-chain, and ads data; create pipelines, semantic layers, and multi-step AI agents; ship full-stack apps and dashboards; collaborate with marketing and client teams to operationalize structured intelligence and measurable impact.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Build and maintain data pipelines, prepare and curate data for ML/GenAI, support presales solution design and BOM/capacity planning, integrate enterprise systems, develop dashboards for deployment tracking and performance, and document standards and automation for scalable AI-enabled infrastructure delivery.

As an AI Data Engineer, you will build and maintain data pipelines and AI models to enhance GTM strategies, improving decision-making and operational efficiency.

Lead enterprise AI adoption by evaluating vendors and models, designing reusable agentic frameworks and reference architectures, developing prototypes and POCs, and enabling business units to deploy secure, scalable LLM-, RAG-, and agent-based solutions across cloud and microservice environments.

Lead design, prototyping, and operationalization of AI/ML solutions for intelligence mission workflows. Develop LLM integrations, scalable architectures, and analytics to automate discovery, accelerate insights, and transition capabilities to production while shaping technical strategy and best practices.

Provide onsite and remote technical support for NVIDIA Ethernet and AI infrastructure, troubleshoot Linux-based systems, debug networking and interoperability issues, collaborate with engineering and marketing, document support methodologies, and act as primary customer contact, spending at least one week per month onsite.

Architect and ship production AI systems and agent workflows with strong focus on explainability, auditability, and compliance for financial services. Build Python/FastAPI tooling, distributed event-driven backends, evaluation and monitoring pipelines, and occasional React/TypeScript UIs. Drive schema, API, and system architecture decisions and establish validation and quality-assurance patterns for AI outputs.

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.

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.

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.