Top Remote AI Engineer Jobs

Designs, builds, and deploys enterprise AI solutions for pharmaceutical and healthcare marketing operations. Leads client discovery, translates business needs into scalable architectures, develops integrations and AI workflows, and delivers production-ready cloud solutions. Responsibilities include generative AI, RAG, data pipelines, enterprise systems integration, authentication, observability, CI/CD, Docker, Kubernetes, and Google Cloud Platform. Serves as a technical advisor while collaborating with product, engineering, data, security, and delivery teams.

Develop and deploy scalable AI and machine learning applications within the OneStream platform. Responsibilities include building software with Python and SQL/NoSQL, troubleshooting full-stack systems, designing complex interfaces, collaborating cross-functionally, and applying data science solutions to products. The role focuses on technologies such as forecasting, anomaly detection, and natural language processing, with opportunities to use cloud platforms, APIs, databases, and machine learning frameworks.

Build and deploy full-stack AI/ML and agentic applications for strategic enterprise customers. Partner with technical and executive stakeholders to move use cases from prototypes to production, design scalable AI architectures, establish reusable solution patterns, and advise on AI roadmaps, governance, deployment, and operations. Mentor internal teams, support enablement, and provide product feedback. The role combines remote and customer-facing engagement with occasional travel up to 20%.

Develop and deploy scalable AI and machine learning applications, including forecasting, anomaly detection, and natural language processing solutions. Design software architectures, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role requires production machine learning experience, strong programming skills, and the ability to learn evolving AI technologies. Preferred experience includes web service frameworks, distributed systems, and Azure cloud computing.

Develop and maintain scalable AI and ML architectures for a life sciences regulatory intelligence platform. Lead technical decisions, integrate LLM services and microservices, build conversational and agentic systems, manage data stores and databases, support production delivery, and collaborate in an Agile Scrum environment. The role requires hands-on Python development, generative AI deployment, cloud architecture, vector and graph databases, hybrid search, and backend API development.

Build and operate production-grade AI data pipelines and financial knowledge services for equities content. Responsibilities include processing text, tables, documents, and multimedia; creating knowledge bases and search indexes; engineering RAG retrieval and citation systems; integrating LLMs and NLP models; managing orchestration, APIs, quality, cost, observability, reliability, and scalable production operations.

Senior AI Engineer responsible for architecting, developing, deploying, monitoring, and optimizing scalable web-based applications and distributed systems. The role includes design and code reviews, performance and stress testing, documentation, technology evaluation, application development, collaboration with business analysts, and mentoring junior developers. Extensive experience with Microsoft technologies, software architecture, database design, performance management, and the full software development lifecycle is required.

Build scalable internal AI solutions, developer tools, agentic workflows, RAG applications, and reusable engineering patterns. Partner with engineering teams to prototype and productionize AI use cases, establish evaluation and responsible-use practices, and support adoption through guidance, documentation, workshops, and enablement. Design for performance, reliability, cost efficiency, privacy, and security while collaborating with architecture, product, and engineering leadership.

Design and implement multi-step agentic AI workflows (orchestration, retrieval, tool use, state management) using frameworks like LangGraph/LangChain. Build retrieval pipelines over proprietary CRE data, evaluate and monitor model quality with human-in-the-loop processes, integrate Anthropic models on AWS Bedrock, implement guardrails and observability, and rapidly prototype features with Product and Design to deliver trustworthy, source-linked AI capabilities that perform real business actions.

Build and optimize Databricks Lakehouse architectures and scalable ETL/ELT pipelines using Spark and Delta Lake across AWS, Azure, and GCP. Tune performance and cloud costs, integrate MLflow for AI model operationalization, and implement CI/CD and Infrastructure-as-Code deployments. Enforce governance, security, and compliance through Unity Catalog. Partner with architects, data scientists, engineers, and clients to deliver production-ready AI solutions and documentation.

Design, develop, deploy, and optimize Generative AI applications using LLMs and AI frameworks. Build scalable APIs, automate workflows, integrate foundation models with enterprise systems, and improve model performance through testing and experimentation. Implement monitoring, quality assurance, governance, security, compliance, documentation, and risk mitigation. Evaluate emerging technologies, support proofs of concept, and collaborate with product, engineering, and business teams.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, supports AI strategy, ensures secure and responsible deployment, mentors engineers, evaluates emerging technologies, and translates business needs into scalable technical architectures.

Build and optimize production-grade agentic AI systems for marketing and enterprise customer experience use cases. Responsibilities include agent orchestration, tool use, RAG, embeddings, guardrails, LLM evaluation, observability, performance optimization, and scalable Python services deployed on AWS. The role partners across product and engineering teams and requires hands-on experience with LLM agents, marketing data platforms, retrieval systems, and AI evaluation.

As an AI Engineer, you will create a unified evaluation infrastructure for AI agents, ensuring reliable performance while integrating automated pipelines and feedback systems for rapid model evaluation and quality assurance.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Design, develop, and deploy AI-powered healthcare solutions using Java and Spring Boot microservices. Implement HL7 FHIR and Da Vinci interoperability standards across prior authorization, claims, eligibility, and provider domains. Build Kafka-based event-driven services, integrate relational databases, automate CI/CD, deploy with Docker and Kubernetes on AWS or Azure, and establish monitoring using Splunk, ELK, Prometheus, and Grafana.

Design, develop, and deploy scalable AI applications and services across the OneStream platform. Build production machine learning solutions, troubleshoot full-stack issues, participate in technical design sessions, and collaborate with cross-functional teams. The role involves applying forecasting, anomaly detection, and natural language processing while maintaining reliable, well-documented code and continuously learning emerging AI technologies.

Design, build, and productionize agentic LLM workflows to improve clinical data extraction quality and reliability. Integrate AI capabilities into production pipelines, ensure scalability and cost-effectiveness, develop evaluation/monitoring for safe deployment in regulated environments, and lead technical design and delivery with cross-team collaboration.

Lead the organization’s AI strategy, architecture, and adoption across a customer-facing insurance platform. Design scalable production AI, LLM, and agentic systems; establish LLMOps, evaluation, observability, governance, and security standards; guide build-versus-buy decisions; and champion AI-assisted development. Partner with engineering, product, and business leaders to redesign enterprise processes around AI while maintaining deterministic, auditable controls for policy, financial, and compliance workflows.

Build and operate governed, production-grade AI and LLM systems across Velsera’s products and business functions. The role covers multi-cloud model serving, MLOps and LLMOps, evaluation, governance, security, auditability, RAG, agentic workflows, and enterprise integrations. This senior individual contributor will establish reusable AI platform patterns, deliver customer-facing and internal capabilities, support regulated environments, and guide build-versus-buy decisions while collaborating with engineering, product, security, compliance, scientific, and commercial teams.

Design, build, and deploy scalable ML training and evaluation platforms for autonomous driving. Lead architecture and implementation of distributed, high-performance pipelines, drive cross-team prioritization, mentor engineers, and support recruiting and code quality to accelerate ML model development lifecycle.

Lead design and deployment of scalable ML platforms and tools, manage complex technical projects, mentor junior engineers, and collaborate across teams to enhance autonomous driving systems.

Build and deploy production AI agents for real customers. Responsibilities include orchestrating LLM pipelines, integrating APIs and tools, developing React interfaces, implementing RAG and document-processing systems, deploying serverless applications, and monitoring performance. The role requires strong Python or JavaScript skills, hands-on OpenAI and multimodal LLM experience, vector databases, agent frameworks, function calling, and full-stack integration. Applicants must be pursuing a Master's or PhD in a technical field.

Build and improve production generative AI systems that create editable motion from natural language. Responsibilities include model orchestration, structured generation, validation, compiler-backed quality gates, fine-tuning, post-training, synthetic data, evaluation, dataset governance, regression testing, and production reliability. The role requires hands-on ownership across LLM behavior, data pipelines, infrastructure, inference optimization, and product quality, with code generation, compilers, multimodal systems, or animation experience being valuable.

Own technical relationships with strategic enterprise customers throughout Moveworks implementations. Design AI agent solutions, enterprise integrations, workflows, and automations using APIs, iPaaS tools, cloud functions, and scripting. Advise customers on agentic AI roadmaps, coordinate cross-functional delivery, develop reusable capabilities, and mentor engineers. The role combines solution architecture, hands-on implementation, technical consulting, executive communication, and up to 25% travel.