Top AI Engineer Jobs

Build and operate agentic AI systems that analyze large-scale time-series telemetry. Own product features end-to-end—frontend, APIs, and scalable agent runtime—work with customers, integrate frontier models, implement sandboxed execution and observability, and measure agent impact in production.

Own and ship agentic AI features across the full stack: frontend chat surfaces, backend APIs and MCP server, scalable agent orchestration, sandboxed code execution, context management, evaluation suites, and production observability. Partner with customers and product to translate telemetry review workflows into dependable agent capabilities and iterate from MVP to production at scale.

Full‑stack product engineer building and operating agentic AI systems that analyze large-scale time-series telemetry. Own product features end-to-end: frontend UX, backend APIs, agent orchestration, sandboxed code execution, evaluation, observability, and model integrations while working directly with customers.

Build and operate agentic AI systems for hardware telemetry: design frontend surfaces, backend APIs, and scalable orchestration. Own product development from customer discovery through shipping and run evaluation, observability, and model integrations for reliable agent behavior.

Lead frontend-focused development for a conversational AI assistant: design and build UX-forward features, apply LLM and agentic system knowledge, prototype interaction patterns, implement frontend (TypeScript/React) and supporting backend APIs, mentor engineers, and troubleshoot AI-specific production issues like bad model outputs and streaming failures.

Work on-site with a DHS client to design, develop, train, fine-tune, and deploy AI/ML and generative AI solutions (including LLMs and NLP). Build user-facing AI applications (chatbots, virtual assistants), optimize model performance and scalability, implement MLOps/DevSecOps pipelines, conduct research (prompt engineering, RAGs, fine-tuning), ensure data privacy/security, and brief stakeholders on technical tradeoffs.

Lead design and build of a scalable analytical data platform: develop and optimize ETL/ELT pipelines, create analytical data models, enforce data quality and governance, collaborate with data scientists and analysts, mentor engineers, and drive continuous improvement of analytics tooling and processes.

Lead architecture and product-aligned engineering for agentic, event-driven AI systems handling tens of millions of unstructured documents. Drive platform-product integration, accelerate the SDLC with AI tooling, solve critical scalability bottlenecks, and mentor engineering teams while aligning technical strategy with executive leadership.

Build and scale frontier AI systems for biology: design model architectures, run large-scale pretraining on GPU clusters, develop RL/reward modeling and multi-agent systems, create evaluation frameworks tied to biological outcomes, and publish/open-source research.

Operate and scale multi-site multi-GPU clusters running Slurm on Kubernetes: ensure reliability and observability, debug storage/network/GPU failures, automate capacity and lifecycle management, drive configuration-as-code, partner with researchers and vendors, and reduce incident MTTR while enabling multi-thousand GPU training runs.

Develop and scale deep learning foundation models and systems for biology. Design model architectures and pre-training pipelines, build post-training discovery systems (RL, reward modeling, reasoning, multi-agent orchestration), create evaluation frameworks tied to biological outcomes, and publish/open-source results while collaborating with scientists worldwide.

Design and build a secure, scalable platform for AI-enabled applications to integrate with Zipline production systems. Implement reusable APIs, gateways, middleware, and guardrails (auth, rate limits, observability) while partnering with AI engineers and service teams to ensure reliability, ownership, and safe production deployments.

Build and operate regulator-grade, production AI-enabled full‑stack applications: real-time audit dashboards, CAAT/continuous-monitoring surfaces, and agentic human-in-the-loop apps. Own end-to-end stack from data modeling and APIs to React/Next.js UIs, ensure responsible-AI practices, data protection, audit trails, and drive adoption with non-technical auditors.

Own full sales and partnership cycle to bring voice AI to mobile service robots: engage senior robotics decision-makers, qualify and prioritize opportunities, close deals, and shape the commercial playbook while operating autonomously within a small technical team.

Lead technical design and delivery of enterprise-facing AI products end-to-end. Partner with product, design, sales, and research to turn enterprise workflows into shipped features, set architecture and standards, mentor engineers, and build extensible integrations, plugins, and multi-step agents for organizational use.

Lead and operate enterprise Azure and Microsoft 365 platforms, manage hybrid identity (Entra ID/AD), deliver and govern AI solutions (Azure OpenAI, Azure ML, Copilot), provide L2/3 support, secure and govern cloud resources, manage on‑prem infrastructure, and drive vendor, project, and stakeholder engagement to scale AI initiatives.

Build and maintain cloud infrastructure and observability for scalable systems. Monitor, troubleshoot, patch, and automate operations using IaC, CI/CD, Kubernetes, and AI-driven automation. Manage backups, disaster recovery, log pipelines, databases, and message streaming. Collaborate with engineering, security, and business teams to improve reliability, incident response, and infrastructure lifecycle (including Terraform state cleanup and decommissioning).

Lead and operate technical support and escalation for Rosalind and life sciences customers. Own complex cases from intake to resolution, diagnose product and configuration issues across ChatGPT, Codex, and the OpenAI API, build bioscience-aware intake flows, playbooks, and knowledge, coordinate cross-functional escalations, and drive durable fixes, automation, and product improvements while maintaining boundaries around scientific consultation.

Early-career full stack internship developing frontend and backend features under senior guidance. Integrate AI coding tools into workflows, translate product requirements to implementations, contribute across the product lifecycle, and suggest cross-team process improvements while learning full-stack practices.

Design, build, test and support batch and streaming data pipelines and curated datasets on a modern Lakehouse and AI data platform. Responsibilities include data modelling, optimisation, quality controls, reconciliation, platform tooling, and partnering with stakeholders to deliver reliable, production-ready data products.

Design, build, test and support batch and streaming data pipelines and curated datasets on a lakehouse/AI data platform. Work across ingestion, transformation, modeling, optimization, data quality and reconciliation; improve platform tooling and standards; collaborate with stakeholders and lead delivery and technical design for workstreams.

Designs, builds, and operates GenAI cloud platforms across Azure, AWS, and Google. Develops self-service tools, CI/CD pipelines, and Terraform IaC. Troubleshoots performance and availability, automates deployments, supports developer adoption of LLMs and AI services, and leads postmortems and scalability planning.

Drive enterprise adoption of Generative AI and LLMs by discovering and prioritizing use cases, enabling teams via training and coaching, configuring and supporting AI workflows and agents, collaborating with technical and compliance teams, and establishing governance and success metrics.

Own reliability, observability, and delivery for a cloud-native, multi-tenant Kubernetes platform. Build CI/CD, GitOps, IaC, and AI-first automation for incident response and LLM infrastructure. Instrument SLOs and tracing, harden secrets and supply-chain security, troubleshoot production issues, and mentor engineers in AI-driven operations.

Design, build, deploy, and operate production LLM-based solutions and agent workflows. Own reference architecture, integrations, IaC, CI/CD, monitoring, governance, and PHI-safe patterns. Lead cross-functional AI initiatives, vendor evaluations, and mentor engineers to scale secure, measurable AI tooling across the enterprise.