Top Remote AI Engineer Jobs

Design, build, and operate AWS-based microservices and asynchronous workflows powering AI-driven laboratory protocol generation. Develop NLP pipelines, classifiers, embedding and RAG infrastructure, evaluation frameworks, guardrails, cost monitoring, and observability. Integrate AI systems into production SaaS architecture, ensure reliability and safe scaling, and support post-launch operations through debugging, incident response, and continuous improvement.

Build reusable AI developer tools, RAG knowledge systems, and AI-assisted SDLC workflows. Integrate coding assistants and design-to-code tooling, define secure observable patterns, and own reliability, usage, and cost for internal AI platforms.

Lead technical direction and people management for an AI/ML engineering squad. Design, build, and deploy production-grade ML and Generative AI solutions (LLMs, RAG, agentic systems), drive MLOps/CI-CD, ensure Responsible AI and HIPAA-compliant practices, conduct architecture/code reviews, and mentor engineers to deliver measurable business outcomes.

Develop and evaluate ML and perception models for future sensing and autonomous driving. Work on detection, tracking, classification, sensor fusion, training pipelines, validation, robustness metrics, and integrate models into onboard systems while collaborating with cross-functional teams.

Lead technical vision and architecture for ML-driven trajectory generation in autonomous vehicles. Build and deploy scalable training pipelines, integrate models into real-time safety-critical onboard systems, mentor senior engineers, drive cross-functional initiatives, and move solutions from research to production using simulation and large-scale datasets.

Lead end-to-end ML and perception efforts for future sensing in autonomous vehicles: design sensor architectures, multi-modal fusion, model development, metrics, simulation-driven evaluation, system integration, and production readiness while mentoring engineers and building scalable data and training infrastructure.

Design and build scalable AI platform capabilities to enable multi-step GenAI workflows, model execution, and integrations. Implement backend services, execution infrastructure, APIs, and observability to support production GenAI experiences across sales and product teams. Collaborate with AI engineers, data scientists, and product partners to turn AI use cases into reliable, extensible platform features.

Develop and operate production AI framework components, runtimes, benchmarking systems, performance tools, and integrations. Benchmark and optimize large language model training and inference across GPUs and Microsoft hardware, build automation and observability, investigate cross-stack performance and reliability issues, and deliver scalable platform improvements. Principal engineers additionally set technical direction, lead multi-team initiatives, define architectures, and mentor technical leaders.

Design, build, deploy, and operate production AI agents for Cisco Commerce Operations. Responsibilities include agent orchestration, RAG pipelines, enterprise API integrations, guardrails, evaluation, observability, CI/CD, monitoring, cost tracking, and continuous optimization. The role partners with engineering and business teams to automate operational workflows and measure business impact.

Lead enterprise AI initiatives as a principal engineer and solution architect. Design, prototype, and deploy scalable generative AI, RAG, agentic AI, and predictive solutions. Translate business needs into technical requirements, evaluate technologies and vendors, conduct architecture reviews, and establish AI engineering, trust, safety, monitoring, and compliance standards. Mentor engineers and guide responsible AI practices across customer-facing products and internal operations.

Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.

Leads architecture and hands-on development for AI frameworks, performance systems, benchmarking, and developer tools. Investigates issues across models, compilers, runtimes, services, and accelerator hardware; improves model onboarding, runtime performance, reliability, hardware utilization, and Azure efficiency. Establishes scalable measurement and observability capabilities, influences cross-team technical strategy, delivers production solutions, and mentors engineers while advancing engineering quality and maintainability.

Lead technical pre-sales engagements for Microsoft cloud database and analytics products. Deliver PoCs, demos, hackathons, and architecture workshops; design secure, scalable solutions; resolve technical blockers; collaborate with engineering; and evangelize Fabric, Databricks, Purview and Azure database technologies to accelerate customer migrations and adoption.

Lead technical pre-sales engagements for Microsoft 365 Copilot, Copilot Studio, AI agents, and cloud endpoint solutions. Deliver demonstrations, discovery sessions, whiteboarding, solution designs, and proof-of-concept support. Develop technical win strategies, address objections and blockers, collaborate with partners and internal teams, educate customers, and guide adoption while embedding governance, security, and compliance into proposed solutions.

Drive technical sales for Microsoft’s cloud and AI data platform through demos, proofs of concept, hackathons, and architecture workshops. Advise enterprise customers on secure, scalable database and analytics architectures, migrations, and deployments. Build relationships with platform leaders, resolve technical blockers, collaborate with engineering, and promote Azure data technologies including Fabric, Databricks, Purview, and Azure databases.

Lead architecture and hands-on development of Microsoft's AI Safety Platform, building observability, detection, investigation, threat-hunting, and distributed data-processing capabilities. Design attack detection infrastructure, correlate high-volume telemetry, apply AI security research, conduct security and privacy reviews, and partner across engineering, science, and incident response teams. Mentor engineers and provide technical leadership for complex AI security systems.

Build and operate production LLM applications, agentic workflows, RAG systems, classification pipelines, MCP servers, and AI tool integrations. Own solutions from architecture through deployment, evaluation, and monitoring in AWS and Databricks. Evaluate emerging AI technologies, lead pilots, and improve team capabilities through demos, working sessions, and pair programming. Apply strong Python engineering, testing, CI, and communication skills to commercial publishing data and operational problems.

Develops, maintains, tests, and supports enterprise applications and integrations within WMATA’s Digital Modernization portfolio. Builds scalable data pipelines and cloud-based solutions using OCI, Azure, Lakehouse architecture, and Autonomous Data Warehouse. Provides technical leadership, resolves complex system problems, manages configuration and version control, performs failure analysis, coordinates projects, prepares technical reports, and supports system reliability, production monitoring, and emergency outages.

Build and ship production AI capabilities using AWS Bedrock, including clinical document extraction, summarization, retrieval, decision support, and workflow automation. Develop Python and FastAPI applications, integrations, tests, CI gates, and evaluation systems. Work with healthcare data standards, PHI, AWS, MySQL, and server-rendered front ends. Use and verify AI coding tools while communicating tradeoffs clearly in a remote, Linux-based environment.

Build and ship full-stack features for an AI agent platform, rapidly prototype and iterate with pilot customers, and work hands-on with LLMs, agents, voice AI, and modern AI coding tools. The role also expects product ownership, independent opinions, strong engineering fundamentals, comfort with ambiguity, and a demonstrated passion for building through projects, hackathons, or open source.

Partners with clients to identify AI opportunities, translate ambiguous business needs into technical requirements, and build solutions using Microsoft Power Platform and Copilot Studio. Develops prompts, grounding strategies, integrations, and prototypes involving Copilot and Claude. Supports testing, quality assurance, maintenance, troubleshooting, documentation, user training, governance, and data protection in regulated environments while building stakeholder consensus.

Architect, build, deploy, and operate production-grade AI agentic systems and workflows. Responsibilities include backend and full-stack development, agent orchestration, workflow state, retrieval, memory, approvals, failure recovery, data architecture, enterprise integrations, cloud infrastructure, observability, security, and scaling. The role establishes reusable engineering patterns and partners with users to deliver reliable AI products, while addressing production issues involving model behavior, permissions, latency, cost, and consistency.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Design and build real-time, highly concurrent software and cloud-native systems for an AI-powered emergency communications platform. Integrate and deploy LLMs, optimize voice and audio-processing pipelines, and support speech recognition, synthesis, detection, and filtering. Contribute to infrastructure, observability, security, scalability, and fault tolerance. Provide technical leadership through architecture decisions, code reviews, mentoring, and engineering best practices.

Build and operate scalable backend services for connected devices, including device connectivity, telemetry ingestion, health monitoring, command and control, and data pipelines. Collaborate with firmware, mobile, and product teams on APIs and workflows. Use AWS, microservices, streaming systems, observability tools, and AI coding agents to deliver reliable features, participate in incident response, and improve automation and engineering practices.