Top AI Engineer Jobs

Architect and deploy production-grade generative and agentic AI systems for Citi’s regulated enterprise environment. Responsibilities include context engineering, prompt engineering, RAG, knowledge graphs, Graph RAG, multi-agent orchestration, MCP and A2A integrations, observability, responsible AI controls, scalable deployment, and real-time AI solutions. The role partners with cross-functional stakeholders, mentors engineers, conducts code reviews, and advances automation across Controls Technology.

Own the conversational intelligence layer for an agentic travel platform. Responsibilities include designing agent architecture, prompts, tool calling, state and memory systems, ranking and retrieval, personalized recommendations, streaming, error recovery, model-provider strategy, evaluation systems, and rendering. The role requires production experience with LLM-powered systems, diagnosing reliability issues, and improving quality through user evidence. It is a full-time hybrid position based regularly in New York City.

Build and improve state-of-the-art agent systems: design RL/RLHF learning pipelines, create custom environments, set up robust evaluation, train smaller task-specific models, leverage open-source text and image models, and help shape technical and hiring strategy alongside founders.

Design and implement intelligent automation for AV software development, testing, release, and operational workflows. Build CI/CD pipelines integrating simulation and hardware-in-loop testing, develop engineering data pipelines and metrics, and create AI-enabled tools for test analysis, failure classification, defect triage, reporting, and workflow orchestration. Establish evaluation, governance, security, and monitoring standards for AI solutions while improving release readiness, software quality, engineering productivity, and cross-functional visibility.

Architects and delivers enterprise-scale AI security systems for identity risk, anomalous access, malware detection, sensitive data classification, and threat recognition. Builds inference engines, ML platforms, feature pipelines, and production model-monitoring workflows. Provides technical leadership across multi-quarter initiatives, integrates AI with access-control and security infrastructure, evaluates emerging techniques such as LLMs and agentic systems, and collaborates across identity, threat detection, and platform teams.

Build enterprise applications and polished user interfaces for agentic AI experiences. Own full-stack features from frontend architecture through backend integration, testing, deployment, and production support. Develop React and TypeScript interfaces for multi-step agent workflows, tool calling, approvals, retrieval, state management, errors, and human-in-the-loop interactions. Integrate APIs, agent orchestration layers, and enterprise systems while contributing to backend development, architecture reviews, reliability, and production engineering.

Leads full-stack engineering and AI modernization initiatives, designing scalable enterprise applications and architecture. Responsibilities include coding, testing, technical discovery, application modernization, security and privacy integration, engineering standards, mentoring, stakeholder consulting, and strategic technology leadership. The role requires collaboration across product, operations, data, and security teams while balancing innovation, platform stability, and delivery commitments.

Lead and guide agile teams to design, build, and deliver secure, scalable AI-enabled software. Develop and review production code, drive product and technical decisions, champion enterprise AI-assisted development practices, implement RAG/LLM architectures and vector stores, integrate cloud services and CI/CD, and establish responsible AI guardrails and validation standards.

Build customer integrations, data syncs, AI workflows, internal agents, and automations for permitting operations. Develop evaluations and monitoring to ensure AI reliability, security, and adoption. Work directly with customers, Permit Ops, Product, and Engineering to identify process improvements, turn one-off solutions into reusable tools, and continuously improve production systems.

Embed with customer AI teams to design, prototype, and productionize AI agents using W&B Weave. Advise on architecture, build reference implementations and demos, run technical workshops, gather field feedback for product roadmap, and troubleshoot customer environments.

Design, develop, and productionize LLM and multimodal AI solutions (RAG, agents). Lead internal AI enablement through training, playbooks, office hours, and cross‑functional collaboration to ensure responsible, scalable AI adoption.

Leads and contributes hands-on to development of an AI engineering platform for semiconductor testing. Designs agent tooling, RAG systems, cloud and air-gapped inference infrastructure, Kubernetes platforms, reusable AI patterns, and agent-facing UIs. Sets technical direction, selects hardware and inference stacks, manages production systems, resolves cross-team architecture decisions, and guides a small engineering team. Requires strong distributed systems, software development, leadership, communication, and LLM/agent tooling expertise.

Lead the technical strategy, architecture, modernization, and company-wide adoption of Pinterest’s AI/ML evaluation platform. Design reliable evaluation systems for GenAI and machine learning workloads using automated metrics, human feedback, expert judgment, safety assessments, and business outcomes. Drive cross-team workstreams, resolve technical tradeoffs, establish evaluation standards, and improve the quality, reliability, and trustworthiness of AI-powered products.

Build and ship production generative AI applications, services, agents, and tools using LLM APIs. Develop prompts, structured outputs, tool calling, retrieval, evaluation frameworks, and failure handling. Integrate AI with product APIs and data models, write optimized SQL, deploy cloud services, operate Hugging Face and hosted inference systems, and monitor quality, latency, token usage, and cost. Collaborate with product and engineering teams while contributing tests, code reviews, and internal AI initiatives.

Lead architecture and hands-on development for dexterous robotics software and Physical AI infrastructure. Own manipulation use cases from definition through demonstration, integrating sensing, control, simulation, learned policies, data pipelines, testing, and deployment. Translate ambiguous R&D goals into delivery plans, establish production-grade reliability practices, coordinate cross-functional technical work, and mentor engineers. Develop core software in C++ and Python using ROS 2 while addressing timing, synchronization, safety, reproducibility, and performance constraints.

Develop and demonstrate AI-enabled robotic systems using robot arms, sensor-equipped hands, and application software. Integrate and troubleshoot mechanical, electrical, sensing, control, and AI components; evaluate robot behavior and sensor data; maintain reliable demonstration systems; and support customer evaluations, trade shows, and live troubleshooting. Translate customer feedback and research objectives into demonstrator requirements, test plans, and engineering improvements.

Embeds with hardware engineering teams to build agentic workflows and AI skills that perform engineering tasks, including requirements checks, test-plan analysis, and issue investigation. Demonstrates value to customers, identifies reusable patterns, and translates customer needs into product capabilities. Requires strong Python and data skills, customer-facing communication, modern AI coding-agent experience, and willingness to travel. Hardware, test, controls, forward-deployed, solutions engineering, and security-clearance experience are advantageous.

Build and operate LLM-backed pipelines for document extraction, classification, and validation. Develop evaluation datasets, regression suites, metrics, and human review workflows to improve AI accuracy. Own production reliability through prompt versioning, structured output validation, cost and latency optimization, retries, and fallbacks. Collaborate with litigation management stakeholders to identify high-value AI applications and deliver reliable systems for processing complex medical records and claim files.

Build and optimize batch and streaming data pipelines for AI, analytics, and enterprise platforms. Develop scalable transformations across cloud and lakehouse environments, implement data modeling, lineage, metadata, quality controls, and orchestration, and resolve complex performance and reliability issues. Partner with cross-functional teams to deliver secure, governed data solutions while mentoring engineers and leading modern data engineering initiatives.

Build and deliver enterprise Generative AI platform services, APIs, microservices, and reusable frameworks. Implement CI/CD, automated tests, model deployment, inference pipelines, observability, and scalable secure solutions while collaborating with architects, data scientists, and stakeholders.

Build and maintain scalable full-stack integrations between Commure and healthcare systems, including EHRs and insurance providers. Design end-to-end workflows, use LLMs to reverse-engineer EHRs, improve architecture and reliability, troubleshoot production issues, implement monitoring, and ensure HIPAA, HL7, and FHIR compliance. Collaborate with product, healthcare, DevOps, QA, and security teams throughout development and deployment.

Build, test, deploy, and productionize AI agents and applications using Microsoft’s AI and cloud stack. Lead customer engagements from discovery through deployment and handoff, applying security, data-boundary, performance, grounding, retrieval, workflow, and governance standards. Collaborate with customers, partners, architects, and practice leads; contribute reusable components, marketplace offerings, and enablement content. Coach engineers and partner delivery teams while helping refine reference architectures and delivery standards.

Lead technical vision and architecture for enterprise ML platforms and MLOps. Design training, feature, serving, and observability systems; establish ML lifecycle and governance; solve complex cross-team engineering problems; mentor senior engineers; evaluate emerging AI technologies; drive cost optimization, compliance, and organizational alignment.

Leads Wasabi’s AI infrastructure, data factory, and developer partnerships strategy. Hunts and originates new relationships, develops joint go-to-market and co-selling motions, structures revenue-generating deals, and manages executive partner relationships. The role collaborates with Business Development, Sales, Marketing, and Product, builds the developer ecosystem, tracks pipeline and revenue performance, and represents Wasabi at industry events. Candidates need deep AI infrastructure and storage expertise, an established industry network, and a proven record of closing strategic partnerships.

Leads the people function through rapid company growth, partnering with the CEO on organizational design, executive hiring, workforce planning, compensation, equity, leveling, leadership development, performance management, talent acquisition, people operations, compliance, employee relations, and the transition from a PEO to internal HR infrastructure.