Top Remote AI Engineer Jobs

Designs and deploys AI/HPC networking infrastructure for customers, including Linux GPU systems, network fabrics, rack layouts, automation, monitoring, and data center upgrades. Supports service reliability, performance optimization, and lifecycle operations. Partners with sales, engineering, support, and customers on solution design, requirements, deployments, product feedback, technical content, training, and roadmap planning. Requires strong data center networking, solution architecture, communication, and customer-facing skills, with up to 20% domestic and international travel.

Design, build, and maintain large-scale AI/ML platform and infrastructure for training, inference, fine-tuning, and Agentic AI. Develop tools, APIs, reliability metrics, and root-cause analyses across application to hardware layers while improving efficiency, resiliency, monitoring, and observability.

Designs, deploys, and operates secure, scalable, fault-tolerant cloud-native applications and infrastructure. Responsibilities include cloud architecture, DevOps, automation, monitoring, performance optimization, deployment troubleshooting, documentation, security, governance, and advising development teams. The role requires collaboration with technical experts, independent problem-solving, operational process improvement, and adherence to banking risk-management standards.

Design, prototype, and productionize applied AI capabilities (LLMs, RAG, agents) to improve reasoning, explainability, and developer productivity. Optimize retrieval, embeddings, multi-agent workflows, and evaluation frameworks; build benchmarks and diagnostics. Collaborate with architects and engineers to ingest enterprise knowledge, refine semantic search, and transition experiments into FedRAMP-aligned production for federal healthcare modernization.

Lead end-to-end front-end development for an enterprise Agentic AI platform. Design interactive, explainable UIs that render streaming LLM outputs, code citations, dependency graphs and authoring workflows. Build real-time experiences with WebSockets/streaming APIs, ensure traceability to source code, collaborate with AI engineers and designers, and drive accessibility, performance, and observability across a FedRAMP-aligned federal deployment.

The Senior AI Engineer designs and deploys AI agents and workflows, collaborating across teams to deliver reliable AI solutions that drive business outcomes.

Lead client engagements applying generative AI to business workflows, especially in regulated mortgage environments. Responsibilities include scoping use cases, building prototypes and proofs of concept, facilitating discovery sessions and learning labs, developing prompts and reusable solution artifacts, creating enablement materials, advising stakeholders on AI adoption, and collaborating with compliance and technical teams to scale successful experiments.

Leads architecture and hands-on development of Java and AWS cloud solutions, including microservices, distributed systems, APIs, and AI-enabled applications. Defines technical strategies, engineering standards, and roadmaps; contributes to coding, reviews, troubleshooting, and deployment. Partners with clients and delivery teams, mentors engineers, supports consulting proposals, and drives scalable, secure, resilient solutions using modern DevOps and agile practices.

Design, develop, and support scalable full-stack, cloud-native applications using Next.js, TypeScript, .NET, Python, AWS, and Kubernetes. Build AI-enabled solutions involving generative AI, LLMs, intelligent assistants, RAG, and workflow automation. Collaborate with architects, product teams, and clients, contribute to architecture and Agile delivery, troubleshoot production systems, establish observability practices, review code, and mentor junior engineers.

Design, develop, test, document, deploy, and support scalable full-stack applications using React, C#/.NET, and Hack. Build backend services, APIs, integrations, and AI-enabled capabilities including LLM workflows. Participate in Agile planning, code reviews, troubleshooting, client communication, and software delivery while applying modern architecture, DevOps, CI/CD, and automated testing practices.

Operate and improve large-scale HPC GPU clusters for AI model training and inference. Build observability, automation, CI/CD, and incident response tooling; lead on-call rotations, ensure security/compliance, collaborate with ML engineers, and optimize capacity and costs for production ML workloads.

Develop and deploy end-to-end AI-powered healthcare applications across front-end, back-end, data pipelines, and model integration. Build LLM, RAG, voice, image, and document-processing solutions; fine-tune and serve GPT and Llama models; create AI agents and copilots; optimize prompts, evaluation, cost, and performance; and collaborate with technical and business stakeholders.

Build and deploy AI-enabled automation solutions across enterprise systems. Partner with Business Applications, Data & Analytics, and IT teams to analyze processes, identify automation opportunities, integrate platforms, test and monitor solutions, document implementations, and train process owners. The role requires hands-on coding, AI workflow experience, enterprise integrations, scripting, SQL, structured data knowledge, production support, and strong communication with technical and non-technical stakeholders.

Build and productionize AI-powered internal automation tools, including RAG workflows, intelligent agents, integrations, and reusable automation components. Partner with Sales, Marketing, and Finance AI Champions to identify workflow bottlenecks and enable safe self-service. Improve TDD, CI/CD, infrastructure-as-code, monitoring, logging, and observability practices while maintaining reliable internal APIs and workflows. Establish a scalable component library and operational model for enterprise automation.

Build and operate production-quality AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling systems. Optimize large language model training and inference across GPUs and Microsoft hardware, diagnose cross-stack reliability and performance issues, and improve model onboarding, hardware utilization, Azure efficiency, and deployment speed. Senior engineers own major components and projects; Principal engineers define technical direction, architecture, and multi-release strategy while leading cross-team initiatives and mentoring technical leaders.

Designs and maintains automated UI, API, end-to-end, and integration tests across React, Angular, and .NET applications. Validates data flows with SQL Server or Azure SQL, integrates test suites into Azure DevOps quality gates, and manages test data for parallel execution. Uses AI tools to accelerate test generation while critically reviewing coverage and assertion quality. Documents defects in Jira, contributes to test planning and acceptance criteria, and collaborates with engineers and offshore QE teams.

Lead development of a patient-facing conversational AI agent for chronic disease management. Build scalable, low-latency voice and multimodal systems, engineer prompts, define evaluation metrics, develop fine-tuning data pipelines, deploy AI models and APIs, assess emerging LLM technologies, and collaborate with clinical experts to validate medical accuracy. Develop backend services using FastAPI and Pydantic while supporting secure, reliable medical AI infrastructure.

This role involves designing, developing, and maintaining AI/ML products, collaborating with stakeholders, and ensuring model deployment on AWS. Responsibilities include creating APIs, establishing MLOps practices, and driving AI strategy for operational improvements.

Design, build, and deploy AI-powered features for a digital banking platform, including natural language interfaces, assistants, and automated workflows. Integrate LLMs and AI APIs using prompt engineering, tool calling, validation, and safety guardrails. Evaluate AI outputs for accuracy, reliability, and regulatory compliance; collaborate cross-functionally; document best practices; conduct code reviews; and assess emerging AI tools and frameworks.

Build and own production-ready AI-powered features across frontend, backend, infrastructure, and deployment. Collaborate with product teams to turn ideas into scalable solutions, design LLM workflows, orchestrate multiple AI models, and optimize latency, cost, and performance. The role also establishes AI-assisted engineering practices and contributes to usability, product quality, architecture, and business outcomes.

Design, build, and deploy AI/ML and generative AI solutions and full-stack applications. Develop frontend experiences, backend services, microservices, APIs, and cloud-native data integrations. Implement RAG, LLMs, vector DBs, dashboards, reporting, and self-service analytics to support quality, patient safety, and workflow automation. Provide technical leadership, mentor engineers, and apply CI/CD, testing, observability, security, and performance best practices.

Build and deploy production micro-apps and LLM-powered agents on a governed GCP platform. Integrate AI with systems (Salesforce, Slack, NetSuite, SQL), harden apps with auth, secrets, CI/CD, logging and monitoring, automate maintenance, and implement evaluation and observability for model quality and drift.

Develop and enhance ServiceNow applications for work order management, scheduling, dispatch, discovery, service mapping, and CMDB operations. Collaborate with stakeholders and technical SMEs, configure event rules, integrate ServiceNow services through REST APIs, manage MID servers, create custom discovery patterns, prevent duplicate configuration items, and configure identification rules and multi-tier service discovery across environments.

Build and operate Lakehouse data pipelines, product features, machine learning and generative AI components, testing, monitoring, instrumentation, and documentation. Collaborate with product managers, engineers, and business users to clarify requirements, deliver production solutions, investigate defects, and improve reliability and performance. Participate in code reviews, technical design discussions, and backlog-driven development while following engineering standards and contributing to scalable Data & AI products.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.