Top Remote AI Engineer Jobs

Owns AI governance submissions from intake through cross-functional review, risk escalation, approval, and compliant implementation. Coordinates Legal, Privacy, Security, Procurement, Cloud, Identity, ServiceNow, and enterprise technology stakeholders; tracks schedules, blockers, documentation, and onboarding requirements. Ensures approved AI products are registered in CSDM, completes post-implementation checks, and improves governance processes, playbooks, and enterprise integrations.

Design, build, and operate production AI and data systems for marketing: LLM/agent orchestration, retrieval pipelines, data pipelines, CDP and warehouse architecture, automation of manual workflows, data quality and reliability, stakeholder collaboration, and mentoring. Ship production-grade Python and SQL solutions and evaluate/monitor AI systems.

As a Forward Deployed Engineer, you will develop AI agents and automation workflows, collaborate with teams, and ensure secure, reliable solutions integrating with various systems.

Lead technical strategy and hands-on delivery of an enterprise AI Security Governance program across visibility, prompt protection, agent control, shadow AI, and compliance. Architect and deploy controls (AI detection/response, DSPM/AISPM, data-path controls), automate security operations, close high-severity AI data and access gaps, and produce executive- and auditor-ready risk narratives while mentoring engineers and collaborating cross-functionally.

Lead the architecture and development of a multi-RAT Open RAN digital twin supporting LTE and 5G NR simulation. Integrate production MAC and scheduler software with PHY, channel, UE, traffic, mobility, interference, and network models. Build fidelity tiers, AI/ML evaluation pipelines, reproducible experiments, calibration and validation practices, automated testing, and CI/CD workflows. Improve simulation scalability and reliability while collaborating across wireless, PHY, AI/ML, software, and lab-validation teams.

As an AI Engineer III, you'll develop agentic AI systems, build LLM-powered features, and operate AI systems in production while collaborating with cross-functional teams.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

Build high-performance, scalable React+TypeScript web applications for payment experiences. Design reusable UI components and optimize frontend performance while collaborating with cross-functional teams on architecture, testing, accessibility, and product delivery.

Embed with enterprise customers to deploy production agentic AI workflows: integrate data sources, identity, CRMs and APIs; handle security, SSO/SCIM, isolated VMs and compliance; drive adoption, instrument usage, and feed product improvements to scale deployments.

Design and build autonomous, enterprise-grounded agentic workflows using RAG and ESRE. Integrate and fine-tune LLMs with internal APIs and SaaS tools. Provision scalable cloud infrastructure via Terraform, implement CI/CD, secure network architectures, and deploy observability and tracing to monitor agent behavior. Provide technical leadership, documentation, and enable internal teams to leverage the Elastic Stack for automated business task completion.

The AI Engineer at BLEN will design and build agentic AI systems, including LLM-powered applications, integrate APIs, and ensure responsible AI practices for federal clients, focusing on performance and observability.

Embed with customers to learn and redesign critical workflows, then build and ship production GenAI/LLM systems on AWS. Own evaluation, deployment, and operational reliability, collaborate with senior stakeholders, and drive measurable business outcomes using Provectus blueprints and agentic tooling.

Designs the technical foundation for Dave’s AI Agent Factory, including agent architecture, MCP servers, governed data access, semantic-layer integrations, evaluation frameworks, deployment standards, observability, authentication, authorization, auditability, and PII protection. The role establishes scalable AI engineering standards and reusable infrastructure while partnering with Data, Engineering, Security, and Product teams to enable safe, reliable company-wide AI adoption.

Build and ship production LLM-powered agents, workflows, and integrations using platform templates, pipelines, and guardrails. Develop comprehensive evaluation suites, integrate business systems and data sources, instrument cost and quality telemetry, and collaborate with business users to iterate within defined scopes. Requires extensive software engineering experience, production LLM application expertise, strong Python and TypeScript skills, API craftsmanship, prompt and context engineering, and a commitment to guardrails and evaluation.

Build and deploy LLM-powered agents and workflows using production templates, pipelines, and guardrails. Develop comprehensive evaluations, integrate AI systems with business data sources, and instrument cost and quality telemetry. Collaborate with business users to rapidly iterate within defined scopes while maintaining production standards. The role requires extensive software engineering experience, production LLM application development, strong Python and TypeScript skills, API expertise, and retrieval or healthcare data experience as desirable qualifications.

Build and deploy LLM-powered agents, workflows, and integrations using production templates, pipelines, and guardrails. Develop comprehensive evaluation suites, integrate with business systems and data sources, and instrument solutions for cost and quality telemetry. Collaborate with business users to iterate rapidly within defined scopes. The role requires strong Python and TypeScript skills, production LLM application experience, API development, prompt and context engineering, and retrieval expertise.

Build and deploy LLM-powered agents and workflows using production templates, pipelines, and guardrails. Develop comprehensive evaluations, integrate with business systems and data sources, instrument cost and quality telemetry, and collaborate with business users to iterate within defined scopes. The role requires strong Python and TypeScript skills, production experience with agents, RAG, and tool use, as well as expertise in prompt and context engineering.

Develops AI/ML prototypes, APIs, microservices, integrations, and lightweight user interfaces for federal and DoD-aligned mission workflows. The role integrates and optimizes machine learning models for dashboards and applications, supports data ingestion and preprocessing, validates performance, and helps transition models into production. Collaboration with data scientists, architects, developers, PMO leadership, and cybersecurity teams is required. The position requires five years of relevant experience, a bachelor’s degree, ITIL v4 Foundation certification, and Public Trust or active Secret clearance.

Design, develop, deploy, and iterate production machine learning and generative AI solutions. Responsibilities include model development, NLP, prediction, anomaly detection, LLM and RAG integration, data pipelines, feature engineering, evaluation, monitoring, API integration, and architecture decisions. The role collaborates cross-functionally to balance accuracy, latency, cost, scalability, and maintainability.

Support basketball operations by developing, deploying, and integrating AI/ML models to inform strategy, player evaluation, and performance. Collaborate with software engineers and data scientists, work with internal/external data and infrastructure, and build systems that drive real-time decision-making.

Build and own BetterHelp’s internal AI enablement stack for engineering, product, and design teams. Develop AI agents, automations, MCP connectors, prompt and skill libraries, and governed knowledge systems. Lead experiments from infrastructure setup through secure deployment and maintenance, using AWS and Terraform. Drive organizational adoption through change management, training, governance, and enablement programs while operating autonomously within a regulated environment.

Own the architecture, implementation, operation, and documentation of Legal AI platforms and integrations across CLM, CRM, workflow, identity, and collaboration systems. Build governed AI plugins, agents, automations, evaluation controls, security mechanisms, observability, CI/CD workflows, and reliable data contracts. Translate legal and compliance requirements into auditable technical controls while preserving human review. Partner with Legal, IT, Security, and business stakeholders to deliver durable production systems, improve self-service contracting, support reporting, and mentor technical collaborators.

Build and lead the organization-wide adoption of generative AI, agentic systems, and AI-assisted development. Design production automation agents, evaluate coding tools, modernize rules-based workflows with ML, and establish responsible AI guardrails. Partner with engineering, product, data, and clinical teams to identify high-impact opportunities, own the enablement roadmap, measure outcomes, and drive adoption through working software, documentation, and hands-on collaboration.

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.