Top AI Engineer Jobs

Develop and deploy applied ML and computer vision models to understand technical drawings and specifications. Work on multimodal reasoning across diagrams, text, and structured data, translate research into production within ~1 year, present prior research, and collaborate closely with founders to build customer-impacting solutions.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Designs, deploys, validates, and operationalizes AI/ML solutions for infrastructure reliability, forecasting, observability, automation, and enterprise decision-making. Builds models, NLP capabilities, data pipelines, APIs, dashboards, and MLOps workflows across hybrid cloud and on-premises environments. Manages the model lifecycle, including governance, validation, monitoring, remediation, and production support. Partners with engineering, operations, risk, cybersecurity, architecture, data science, and product teams while communicating technical outcomes and risks to technical and executive stakeholders.

Leads AI security, systems engineering, platform modernization, and developer productivity initiatives. Designs secure AI adoption strategies, manages AI vulnerability identification and remediation, builds CI/CD and DevSecOps automation, modernizes Java platforms, and enables rapid security releases. Partners across architecture, security, infrastructure, and development teams to establish enterprise engineering standards, improve software delivery, reduce technical debt, and strengthen security posture.

Develop scalable data pipelines, analytics platforms, and machine learning infrastructure. Build and maintain software tools supporting AI-driven platforms, distributed data processing, and intelligent automation. Collaborate with engineering and business teams to deliver reliable, scalable data systems and ML workloads.

Owns the strategy, architecture, and implementation of production AI and machine learning systems for personalization, search ranking, relevance, and patient-provider matching. Builds recommendation engines, retrieval models, LLM applications, and scalable ML infrastructure while establishing AI safety, evaluation, and MLOps standards. The role requires hands-on model development, technical leadership, mentoring, and responsible deployment of AI in a regulated mental healthcare environment.

Leads development of resilient, scalable software and AI/ML platform services using Python and Java. Drives AI-assisted engineering practices, mentors teams, integrates enterprise systems, and builds fraud-detection and risk-assessment ML pipelines. Designs feature-store, model deployment, monitoring, and agentic AI components with production guardrails, governance, security, and auditability. Collaborates with architects, data scientists, business stakeholders, and infrastructure teams in an agile environment.

Build and optimize scalable frontend applications for Seekr’s AI platform using React and Next.js. Collaborate with ML, backend, DevOps, QA, and Product Design teams to rapidly deliver performant web features. Contribute to technical direction, automated testing, API integration, deployments, and continuous improvement of frontend architecture and user experiences.

Monitor and support large-scale AI/ML compute, storage, and network infrastructure in a 24x7 environment. Responsibilities include customer case triage, incident response, SLA impact management, documentation, runbook maintenance, fleet management, firmware and configuration upgrades, planned maintenance orchestration, and collaboration with SRE and product teams. The role requires data center travel for commissioning and troubleshooting, plus flexibility for critical incidents outside regular business hours.

Build enterprise Angular applications for conversational AI, agent workflows, dashboards, and real-time monitoring. Integrate APIs, LLMs, RAG, tool calling, streaming responses, and human-approval workflows. Develop reusable components, manage application state, ensure accessibility, security, performance, and cross-browser compatibility, and write unit and end-to-end tests. Collaborate with product, UX, backend, and AI teams while contributing to technical design, code reviews, and mentoring.

Supervise NOC workflows and frontline response for 24x7 AI cloud infrastructure. Manage compute, storage, and network impacts; handle escalations; lead incident management and prevention; maintain runbooks; drive automation and observability; and collaborate with customers and product teams. The role requires hands-on network and systems operations experience, OpenStack or private-cloud expertise, and travel to data centers for commissioning and troubleshooting.

Build scalable AI capabilities for Fidelity Wealth, partnering with business, product, architecture, data, and platform teams. Develop secure, reliable, production-ready solutions; apply modern software architecture, cloud platforms, testing, and design patterns; lead technical discussions; mentor engineers; and deliver end-to-end full-stack systems.

Designs and validates high-performance AI network fabrics using Teralynx switches, SONiC, RoCE, and advanced Ethernet technologies. Responsibilities include architecting leaf-spine and other topologies, optimizing congestion controls and telemetry, analyzing GPU collective communication, debugging network performance issues, building automated validation frameworks, and correlating network behavior with end-to-end workloads. The role also supports benchmark labs, customer demonstrations, hardware-software integration, and reusable infrastructure solutions.

Build, standardize, and operate compute and software infrastructure for AI and HPC validation labs. Responsibilities include cluster provisioning, Linux images, drivers, Kubernetes or Slurm orchestration, benchmark harnesses, workload libraries, observability, dashboards, incident response, root-cause analysis, and automation. The role partners with architecture and performance teams to mirror customer deployments and support repeatable validation across AI, networking, memory, and DPU use cases.

Own strategic proof-of-concept engagements for frontier AI companies across Stripe’s payments, billing, and monetization products. Responsibilities include customer discovery, solution architecture, rapid prototyping, cross-functional leadership, activation, roadmap influence, and creating reusable implementation patterns. The role requires pragmatic coding, strong API and integration expertise, and the ability to lead ambiguous 0-to-1 technical engagements across pre- and post-sales phases.

Build and deploy production-grade generative AI systems using Python, LLMs, RAG, prompt engineering, and agentic workflows. Develop evaluation and monitoring pipelines, integrate foundation models and AI tooling, troubleshoot technical issues, and collaborate with product and engineering stakeholders. Own medium-complexity features, contribute to architecture and design decisions, participate in code reviews, and mentor junior engineers.

Designs, develops, and supports AI agents and process automation workflows across hybrid enterprise IT environments. Integrates applications through APIs, configures permissions and guardrails, troubleshoots infrastructure and authentication dependencies, and maintains detailed solution documentation. The role also monitors deployed agents, optimizes performance, supports lifecycle management, and collaborates with infrastructure, security, application, and business stakeholders on responsible AI standards and automation improvements.

Develop and deliver native iOS features for MyFitnessPal’s Cal AI food-logging experience. Responsibilities include building interfaces in Swift, optimizing UI, storage, and network performance, implementing tests, researching technical solutions, collaborating with product and design teams, promoting engineering best practices, and mentoring junior developers.

Build and ship production AI agents and LLM-powered systems using modern agent frameworks and orchestration tools. Own technical and product direction, establish agent architecture and observability, implement telemetry, resolve production reliability issues, and apply tool use, retrieval, evaluation, and prompt engineering. Partner with product stakeholders, influence strategy, document technical initiatives, and mentor engineers while delivering full-stack software end to end.

Develop and maintain enterprise-scale platform services for Honeywell Forge and Cognition. Build containerized microservices, APIs, event-driven systems, database integrations, and Kubernetes deployments across multi-cloud and edge environments. Own features through design, implementation, testing, deployment, troubleshooting, and production readiness. Collaborate with architects, SRE, cybersecurity, QA, product, and engineering teams while supporting secure, scalable, and restricted-connectivity deployments.

Designs and maintains secure GitLab CI/CD pipelines, automates vulnerability scanning, hardens and signs Docker images, enforces software supply chain security policies, and secures Kubernetes workloads. The role investigates vulnerabilities, manages Linux security and system hardening, and supports compliance with open-source licensing and security standards. Responsibilities include container security, image optimization, dependency checks, Kubernetes configuration security, and automated remediation across the AI inference platform.

Build production-quality React and TypeScript applications for investment and risk workflows, integrating backend APIs, databases, and AI-powered agents. Develop agentic workflows involving tool calling, context retrieval, multi-step reasoning, and workflow completion. Collaborate with product, architecture, engineering, and business stakeholders to translate requirements into scalable applications. Apply AI coding assistants, LLM patterns, testing, deployment, secure integration, and responsive design practices.

Develop and maintain enterprise-scale full-stack platform services, APIs, microservices, distributed systems, and containerized deployments for Honeywell Forge and Cognition. Responsibilities include event-driven development, database and messaging integration, multi-cloud and edge deployment, Kubernetes operations, automated testing, security, observability, troubleshooting, and production readiness. The engineer owns features from design through deployment and collaborates with architecture, SRE, cybersecurity, QA, product, and engineering teams.

Develop and maintain enterprise-scale full-stack and platform services for Honeywell Forge and Cognition. Responsibilities include designing containerized microservices, REST APIs, event-driven services, distributed systems, cloud and edge deployments, database integrations, Kubernetes services, automated testing, observability, troubleshooting, and secure software delivery across multi-cloud and restricted-connectivity environments.

Lead the architecture and delivery of secure data pipelines, APIs, integrations, and governed data services supporting AI assistants, enterprise search, and agent workflows. Ensure permissions, information barriers, data quality, traceability, and resilient synchronization across enterprise systems. Lead technical delivery, troubleshoot production issues, evaluate retrieval and integration performance, review designs and code, mentor engineers, and translate legal, business, security, and governance requirements into scalable solutions.