Top Remote AI Engineer Jobs

Lead org-wide developer experience and AI-enabled developer workflows. Identify and solve high-leverage bottlenecks across build systems, monorepo tooling, CI/CD, and test reliability. Drive standards, enable monolith-to-modular migration, embed with product teams, mentor engineers, and deliver AI-native developer tooling and adoption at scale.

Lead Conversational AI Developer responsible for developing chatbots and virtual assistants, integrating AI technologies, and improving user interactions through data analysis and software development.

As a Fullstack AI Engineer, you'll prototype frameworks and scale real-time engines, focusing on product feel without handed specs.

Lead design, development, and productionization of ML and LLM systems for patient- and provider-facing healthcare products. Own end-to-end workflows: problem scoping, data, modeling, evaluation, deployment, monitoring, and clinical guardrails. Build evaluation frameworks, infrastructure, and tooling; partner with clinicians and product; mentor engineers and set technical direction to deliver safe, impactful AI features for clinical care.

Lead API product direction for search and retrieval primitives, partner with strategic accounts to architect integrations, translate customer insights into product specs and roadmaps, run technical deep-dives and documentation, and prototype and ship production code to improve developer experience and platform reliability.

As a Senior AI Platform Engineer, you will design and build production software for AI Employees, manage technical delivery for client implementations, and ensure system reliability and performance.

The Founding AI Engineer will design and build an AI-native compliance platform, handling complex financial data and developing scalable backend services, while establishing processes for enterprise readiness.

Design, implement, validate, and deploy advanced machine learning and statistical models across the data science pipeline. Collaborate with product and engineering, mentor peers, translate research into production, and deliver high-impact business insights using large-scale and distributed data platforms.

Partner with enterprise AI teams to design, implement, and scale production-grade generative AI observability and evaluation programs. Lead technical discussions, build custom integrations and tooling, manage multiple client engagements from scoping to delivery, and drive best practices across cross-functional teams to influence product and expand customer impact.

Develop and deploy AI-enabled full-stack web applications on AWS using Java or Python, ReactJS, REST APIs, relational databases, workflow automation, analytics, and accessible interfaces. Support AI features such as intelligent search, chatbots, document processing, summarization, and agent workflows. Participate in Agile delivery, DevSecOps, CI/CD, security scanning, testing, code reviews, troubleshooting, and cross-functional collaboration for federal mission solutions.

Build responsive React + TypeScript frontends and scalable Python/Rust backend services and APIs. Integrate with cloud (AWS/GCP/Azure), design databases, CI/CD, tests, and monitor reliability, performance, security, and cost. Optionally develop LLM/RAG/embeddings/vector database AI features.

Build end-to-end AI-native features: responsive React/TypeScript frontends, Python/Rust backends, cloud integrations (AWS/GCP), databases, CI/CD, observability, and optionally LLM/RAG/embeddings-based AI features while improving reliability, performance, security, and cost.

Build and operate an AI-native software development lifecycle across planning, coding, review, testing, deployment, and incident response. Develop internal AI agents, shared harnesses, CI/CD gates, automated QA, code reviewers, and developer workflows. Embed with engineering teams, ship on their backlogs, drive adoption, remove infrastructure and tooling blockers, and measure improvements in throughput, quality, cycle time, cost, and developer experience.

Build and launch AI-powered prospecting agents, SDR tools, lifecycle automations, onboarding flows, and internal products. Integrate CRM, sales, and marketing platforms using workflow tools, APIs, scripts, and LLMs. Own lead scoring, routing, SDR-to-AE handoffs, and lifecycle optimization. Measure impact on pipeline, meetings, activation, retention, and CAC payback while partnering with Sales, Lifecycle, Data, and Engineering teams.

Build and productionize end-to-end AI-powered product features: prototype and iterate on prompts, retrieval flows, evals, backend systems, APIs, data models, and UX to deliver reliable, customer-facing LLM workflows.

Lead architecture, design, and delivery of enterprise-scale Agentic AI solutions. Oversee multiple engineering pods, mentor teams, manage client-facing architecture discussions, ensure production-grade MLOps, cloud infrastructure, microservices, observability, and adoption of emerging AI technologies.

Drive technical pre-sales for Cloud and AI application solutions: run demos, PoCs, hackathons, and architecture workshops; co-design secure, scalable cloud-native architectures; resolve technical blockers; maintain expertise in AI Foundry, Azure AI, Responsible AI, and AI management/security to accelerate customer adoption and production deployments.

Own product areas for KIRA’s AI Stockbroking and other financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Use research, feedback, metrics, experiments, and production insights to launch features, monitor performance, and drive continuous improvement while balancing speed, experience, compliance, and operational complexity.

Own product areas within KIRA’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; and use research, feedback, experiments, and performance data to iterate after launch.

Own product areas within AI Investing and other financial services, defining user problems, product scope, success metrics, roadmaps, and launch plans. Partner with engineering, design, data, operations, compliance, and business teams to simplify complex financial products, launch features, monitor performance, and iterate using data, feedback, research, and production insights. The role requires strong product judgment, execution, prioritization, communication, and ownership in ambiguous environments.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Simplify complex financial products, use research and performance data to guide decisions, launch features, monitor results, and drive continuous iteration while balancing speed, customer experience, business impact, compliance, and operational complexity.

Own reliability, monitoring, and incident response for AI infrastructure; build automation and CI/CD tooling; manage Kubernetes/Docker production workloads; partner with infrastructure, security, and compliance; improve observability and documentation; develop internal full‑stack tooling in Go or Python.

Senior SRE on the IT Operations team owning reliability, monitoring, and incident response for AI infrastructure. Build automation, CI/CD and Kubernetes tooling, improve observability and documentation, and develop internal full-stack tools using Go or Python. Partner with Infrastructure, Security, and Compliance to scale secure, resilient AI deployment pipelines.

Own the design and implementation of WISEcode’s automated quality engineering ecosystem. Build AI-augmented UI, API, integration, regression, database, performance, and reliability testing frameworks for ASP.NET and Blazor applications. Integrate testing into CI/CD, improve observability and release confidence, investigate failures and race conditions, validate AI/ML outputs, and establish scalable quality standards and metrics across engineering teams.

Build and prototype state-of-the-art large language models and AI assistants, take services from ideation to production, fine-tune open-source models, use LLM platforms, analyze model statistics and projections, and collaborate cross-functionally in a fast-paced hybrid Palo Alto environment.