Top Remote AI Engineer Jobs

Build, secure, and operate an Azure Government AI platform supporting Azure AI Foundry and approved generative AI models. Configure AI projects, deployments, networking, identities, monitoring, cost controls, and integrations. Automate repeatable infrastructure and release processes, troubleshoot across cloud layers, and create operational documentation. The role requires strong Azure engineering, AI platform, security, networking, monitoring, and scripting experience, with responsibility for reliable developer experiences and governed AI usage.

Develop, deploy, and optimize scalable machine learning solutions, including predictive models, deep learning systems, data pipelines, and model-serving infrastructure. Monitor model performance, manage retraining and lifecycle processes, ensure quality and compliance, and support responsible AI practices. Collaborate with data scientists, engineers, product teams, and business stakeholders while researching emerging AI technologies and improving AI architectures.

Design, test, and optimize prompts for Generative AI systems across customer support, content generation, workflow automation, knowledge management, and productivity use cases. Evaluate AI outputs for accuracy, tone, usefulness, and consistency; document findings and maintain prompt libraries and standards. Collaborate with AI engineers, product teams, data scientists, and business stakeholders to translate requirements into prompting solutions while researching emerging AI tools and methodologies.

Leads the architecture, development, deployment, and optimization of enterprise machine learning solutions. Responsibilities include designing scalable ML systems, developing predictive models, supporting MLOps and model lifecycle management, improving data quality and feature engineering, implementing governance and validation frameworks, and mentoring technical teams. The role also contributes to AI strategy, innovation initiatives, responsible AI practices, and organizational modernization.

Designs, tests, optimizes, and manages prompts for large language models and AI applications. Develops reusable prompt templates, evaluates AI outputs, conducts A/B testing, monitors performance, and improves accuracy, consistency, and business relevance. Collaborates with AI engineers, product managers, trainers, and business stakeholders to translate requirements into prompting strategies. Researches generative AI advancements and contributes to prompt engineering standards, evaluation methods, and responsible AI practices.

Build and operate Python-based, multi-agent AI backend systems for go-to-market workflows. Design model-agnostic LLM architectures, tool-use patterns, integrations, observability, evaluation infrastructure, and production services deployed on Kubernetes. Own systems end-to-end, including architecture, deployment, reliability, experimentation, debugging, and incident resolution across CRM, enrichment, outreach, and communication platforms.

Lead and define AI architecture, ensuring scalable, reliable AI systems while mentoring engineers and collaborating with product teams.

Build the enterprise platform for an AI creative product, including workspaces, permissions, SSO, audit logs, usage-based billing, invoicing, payment integrations, and security foundations. Develop backend services, APIs, and polished React/Next.js administrative interfaces for multi-tenant customers. Collaborate with founders, sales, and customers to define and ship enterprise features while taking increasing ownership of platform surfaces.

Build the enterprise platform for an AI creative product, including workspaces, roles, permissions, SSO, audit logging, usage-based billing, invoicing, and payment infrastructure. Design scalable APIs and multi-tenant architectures, strengthen security and reliability, and create React/Next.js administrative experiences. Partner with founders, sales, and customers to deliver enterprise requirements, establish engineering practices, and help shape the enterprise engineering team.

Design and build scalable, automated evaluation pipelines and infrastructure to validate speech, audio, and multimodal models. Translate research benchmarks into enforceable pass/fail gates, run batch and streaming tests, operate canaries and continuous monitoring, integrate quality gates into CI/CD, and partner with research, training, inference, and infra teams to prevent regressions and ensure production model quality.

Owns AI-assisted engineering across accelerator software teams, embedding agentic workflows into coding, debugging, triage, testing, documentation, CI, and silicon-debug processes. Builds shared agent tooling, maintains evaluations, measures productivity gains, and establishes standards for reliable AI-native development. Requires expert experience with Codex, Claude Code, LLM APIs, evaluations, and adopted developer tooling, plus strong systems or infrastructure credibility.

Lead design and delivery of agentic AI systems and LLMOps on AWS to generate, validate, and deploy infrastructure-as-code (Terraform). Build multi-agent applications, RAG pipelines, and AIOps for cloud operations; integrate AI into CI/CD and ITSM. Set technical direction, establish guardrails/policy-as-code, curate reusable Terraform modules, and mentor engineering teams.

Build AI-powered features that automate data-driven slide decks and documents. Develop production LLM solutions using LangChain, RAG, LlamaIndex, and generative AI technologies; improve system performance and reliability; and collaborate with designers and engineers. Responsibilities span AI insights, content generation, integrations, optimization, and potentially LLM agents. The role requires advanced Python, machine learning and NLP expertise, and experience building and fine-tuning LLMs in production.

The role involves developing scalable backend services using Python, optimizing performance, integrating AI features, and collaborating with cross-functional teams.

Build and improve Health Note’s AI-powered Access Agent in production. Responsibilities include developing evaluation systems, dashboards, review workflows, logs, metrics, experiments, regression checks, and tools connecting prompts, configurations, workflows, and outcomes. The role analyzes agent performance and operational results, partners cross-functionally to prioritize improvements, and owns initiatives from investigation through rollout and measurable impact.

Operate and improve enterprise AI/ML platforms across Palantir Foundry, AWS Bedrock, and Amazon SageMaker. Responsibilities include deployment, monitoring, observability, automation, governance, model lifecycle management, incident response, root cause analysis, CI/CD, infrastructure as code, and production reliability. The role provides technical leadership and mentorship while partnering with data, infrastructure, security, architecture, product, and engineering teams to support cloud-based AI workloads and Generative AI adoption.

Own full-stack AI product features from concept through launch, adoption, and iteration. Translate user, partner, and enterprise needs into reliable product experiences involving AI chat, search, research, workflow automation, administration, and integrations. Build and test across front-end, back-end, APIs, and infrastructure-adjacent areas; make product and technical tradeoffs; measure adoption and reliability; and establish reusable engineering patterns. Collaborate with product, design, enterprise teams, leadership, open-source contributors, and external partners in a remote environment.

Lead the design, development, and operation of production-grade automation, agent frameworks, and CI/CD pipelines supporting documentation, localization, and language operations. Integrate platforms through REST APIs and MCPs, apply LLM capabilities responsibly, improve reliability and scalability, establish reusable standards, and mentor engineers. The role is fully remote in the US, with preferred Eastern or Central time zone availability.

Develop and maintain MCP tools and the MCP Toolbox for Databases; expand test coverage for agentic coding assistants; refactor and maintain test frameworks; convert technical posts into production-ready sample code and documentation; build real-world data applications (QueryData) to support product R&D and collaborate with cross-functional teams to accelerate agentic AI adoption.

Design and present agentic AI solutions for enterprise manufacturing and supply-chain processes. Lead process discovery workshops, map KPIs and data flows, configure AI agents using low-code and natural-language tools, and demonstrate business outcomes. Partner with account executives, product, and customer success teams to advance qualified opportunities and deliver customer value without traditional engineering involvement.

Build and maintain evaluation, monitoring, and CI systems to ensure AI quality, reliability, and compliance. Track metrics, detect drift, improve prompting, model selection, and pipelines, productionize robust AI workflows, support audits (SOC 2), and act as a quality gate for AI-related releases.

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.

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.

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.