Top Remote AI Engineer Jobs

Lead identification, scoping, and delivery of production AI features and automation. Prototype and ship LLM-powered solutions, build RAG and agentic pipelines, create low-code workflows, integrate AI into existing systems, and partner cross-functionally to prioritize, deploy, monitor, and iterate on AI initiatives across product, operations, and business teams.

Design, develop, and support cloud-native finance and actuarial applications and integrations. Build AI-powered solutions (LLMs, RAG, conversational agents, document intelligence), automate financial workflows, partner with data teams for pipelines and dashboards, and ensure security, compliance, and operational performance.

Design, build, deploy, and operate production-grade AI agents and RAG solutions. Lead end-to-end ALM: design, testing, CI/CD, security, and customer enablement. Deliver training, workshops, and on-site support while ensuring governance, performance, and adoption.

Design, develop, and deploy ML models and AI applications (search, chat, agentic AI). Perform data preparation, experiment design and analysis, tune algorithms with stakeholders, use AWS data science tools, and collaborate in Agile/DevOps teams to drive business outcomes.

Lead architecture, design, and deployment of low-latency edge AI for vehicle cabins. Optimize and integrate ML models on automotive-grade hardware, collaborate cross-functionally, mentor engineers, and set engineering standards for CI/CD, testing, and performance.

The Senior AI/ML Engineer will enhance developer productivity by improving Python workflows, leading projects, mentoring engineers, and driving best practices.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Lead design, deployment, and operation of production ML systems and inference pipelines. Build and scale APIs and services on Kubernetes, optimize pipelines, monitor model performance and drift, and partner with cross-functional teams to deliver ML insights to customers.

Design and implement AI/ML systems for healthcare, own inference and delivery, manage/scale production ML systems, build scalable data pipelines and APIs, apply MLOps practices (deployment, monitoring, drift detection), and work with large-scale data and distributed computing frameworks.

Build and scale production-grade agentic AI applications: full-stack development, LLM and agent integration, APIs and orchestration, RAG and vector search, model lifecycle management, deployment, monitoring, and reliability with human-in-the-loop workflows and safeguards.

Build and own conversational voice AI products end-to-end: architect, implement, and deploy the platform. Design infrastructure, develop front-end and back-end systems, deliver reliable customer outcomes, and collaborate directly with the CTO to scale the product for enterprise customers.

The role involves developing AI-powered features, including agent capabilities, ensuring their reliability and scalability, and collaborating with cross-functional teams. Candidates should have strong hands-on experience with LLMs and Python in production environments.

As an Applied AI Engineer, you will enhance customer solutions using AI and machine learning, collaborating closely with clients to design effective AI implementations and drive business impact.

Lead client-facing AI engineering engagements: build and deploy production LLM systems using RAG, vector DBs, and agent frameworks; handle cloud deployments, CI/CD, and reliability; debug enterprise issues (auth, networking, data); embed with client teams and document solutions for reuse.

Lead architecture and implementation of autonomous inbound/outbound AI agents, build scalable LLM ops and signal-intelligence data pipelines, integrate identity resolution and third-party CRM systems, and maintain production-ready, well-documented systems for Series B diligence.

The Full Stack AI Engineer will develop and maintain AI agents, build clinician-facing products using React and Python frameworks, and manage data infrastructure to support clinical workflows.

Lead design and build scalable autonomous and semi‑autonomous multi‑agent AI systems. Implement RAG pipelines, tool‑calling agents, memory, observability, and AgentOps. Ensure production readiness, safety, governance, and secure data handling. Provide technical leadership, mentor engineers, and shape platform standards across teams.

Build and deploy generative AI solutions: develop production-grade Python code, LLM-based systems, agent frameworks, RAG pipelines with vector databases, APIs, cloud deployments, and MLOps practices while ensuring model evaluation, guardrails, and observability.

Design, build, and deploy agentic AI and multi-agent systems using LangChain/LangGraph and ADKs. Implement LLM orchestration, RAG pipelines, vector DBs, React-based AI interfaces, and production-grade infrastructure with observability, security, and optimization.

Design, build, and deploy production-grade Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and swapping, model fine-tuning and quantization, scalable FastAPI backends, containerized deployment, and monitoring/optimization for latency, cost, and reliability.

Design, develop, and optimize prompts for conversational AI and integrate them into NLP/LLM systems. Build RESTful APIs (Python/Django/Flask) and React front-ends, evaluate prompt effectiveness with metrics and user feedback, collaborate with product, UX, and data teams, and document prompt engineering methodologies.

Build and maintain scalable full-stack web applications using Spring Boot or Node.js and Python. Develop responsive front-ends, RESTful APIs, integrate AI/ML models, manage SQL/NoSQL databases, and implement CI/CD with GitHub Actions while collaborating with cross-functional teams.

Design and implement agentic, LLM-powered chat systems and workflows using LangGraph/LangChain, vector databases, and RAG. Build scalable, secure architectures, reference implementations, and documentation; collaborate with product and engineering teams; ensure non-functional requirements and responsible AI practices; mentor engineers and promote GenAI best practices.

The AI Engineer will design backend systems for workflows, optimize multi-agent pipelines, develop knowledge retrieval systems, and collaborate to integrate workflows using LLMs and traditional systems.

Build and deploy production AI capabilities for mental healthcare: full‑stack development, agentic systems, RAG and retrieval pipelines, evaluation frameworks, safety guardrails, observability, CI/CD, and cross‑functional collaboration to ensure safe, reliable AI in clinical workflows.