Top Remote AI Engineer Jobs

Design, implement, and deploy scalable ML training and evaluation platforms. Own end-to-end projects, make architectural decisions, drive prioritization, collaborate cross-functionally, and mentor engineers to accelerate autonomous driving model development.

Build and deploy scalable ML training and evaluation platforms, drive technical projects and architectural decisions, collaborate with partner teams, prioritize platform improvements, ensure reliability and performance, and mentor junior engineers.

The role involves optimizing large-scale ML infrastructure for autonomous vehicles, conducting analyses, collaborating with teams, and improving system performance.

Design, build, and own production AI agents and automations for marketing. Integrate and instrument the marketing stack (Salesforce, HubSpot, Qualified, Clay, ZoomInfo, data warehouse), implement predictive scoring and personalization, monitor systems for reliability, and collaborate with marketing, RevOps, and sales to translate problems into deployable software.

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.

Design, build, test, deploy, and maintain cloud-based web applications and microservices. Ensure performance, reliability, and code quality while collaborating on new features, debugging, and optimizing systems. Use AI development tools daily, contribute to infrastructure as code, and promote maintainable architecture and documentation.

Build high-performance API services and production-grade Generative AI systems. Design and implement end-to-end RAG pipelines, vector stores, embeddings, and latency-optimized generators. Develop agentic AI using LangChain/CrewAI/Semantic Kernel, integrate LLM APIs and AWS Bedrock, manage CI/CD and ML pipelines, and apply evaluation frameworks to detect hallucinations and optimize performance.

Build and operate production systems for Juniper Square’s AI Platform, including APIs, infrastructure, reusable frameworks, and AI/LLM capabilities. Own projects end to end, ensure reliability and observability, contribute to technical design, and support incident response. Collaborate with product, design, QA, and engineering teams while applying AI-native development practices. The role requires strong Python backend engineering, cloud-native technologies, databases, production operations, and hands-on experience with RAG, agents, evaluation workflows, or related AI systems.

Build and deploy AI-powered tools and workflows across performance marketing, creative production, CRM, landing pages, growth operations, and product growth. Automate analysis, personalization, content generation, campaign activation, reporting, and conversion optimization. Integrate LLM APIs, marketing platforms, data pipelines, and orchestration tools while mentoring growth teams on AI adoption and partnering cross-functionally to deliver production-grade solutions.

Develop backend CRM/ERP functionality with Python and Django, support Camunda infrastructure, integrate LLM models, and build AI-related services. Design service architectures, make technical decisions, estimate and decompose tasks, conduct code reviews, collaborate with analysis, QA, and support teams, and coordinate development projects. The role requires expertise in PostgreSQL, APIs, high-load systems, Docker, Kubernetes, and CI/CD.

Own presales discovery, solution architecture, technical demos, prototypes, proof-of-concept definition, technical evaluations, RFP responses, and security questionnaires for enterprise AI customers. Translate customer workflows and technical requirements into validated architectures and measurable business value, manage implementation risks, and provide accurate delivery handoffs. Convert field insights into reusable reference architectures, demos, and product improvements.

Own end-to-end AI and platform initiatives, designing scalable applications, agentic workflows, and reusable AI capabilities for loan origination and servicing. Build and operate production LLM systems, model routing, MCP tools, agent handoffs, evaluation frameworks, guardrails, and observability. Establish platform patterns, improve reliability, make technical trade-offs independently, and mentor engineers. The role requires deep Python and backend expertise, SQL proficiency, and experience with modern agent frameworks, RAG, model evaluation, and domain-driven architecture.

Build and maintain backend services, APIs, and integration layers for a modular ecommerce platform. Integrate personalization, semantic and LLM search, DAM, PIM, SAP/HANA, ERP, analytics, and customer data systems. Use AI coding agents throughout development while validating code quality, security, performance, and reliability. Design scalable APIs, automated testing, observability, and reusable architecture while collaborating with internal teams, vendors, and implementation partners.

Own end-to-end AI product development for enterprise clients, from requirements and rapid LLM prototyping through production deployment. Responsibilities include data modeling, ETL pipelines, integrations, evaluation, observability, security, reliability, cost management, and client communication. The role requires shipping real AI products, iterating based on usage, and independently managing ambiguous technical and product decisions.

Lead technical pre-sales engagements to design secure, scalable Azure architectures for migration, modernization, and AI workloads. Build trusted advisor relationships, drive POCs, apply CAF/CMF best practices, design hybrid networking and containerization solutions (AKS, Arc, AVS), and collaborate with engineering and partner teams to overcome technical objections and optimize enterprise migrations.

Build and optimize AI agents and automations across Flosum’s revenue engine. Responsibilities include deploying AI SDR systems, automating lead qualification and CRM workflows, integrating Salesforce with GTM platforms, creating competitive intelligence and personalization systems, improving pipeline conversion, predicting customer churn, supporting product-led growth, and establishing feedback loops for continuous agent refinement. The role requires hands-on coding, AI orchestration, GTM systems expertise, and close collaboration with the CEO and revenue teams.

Designs and integrates AI-enabled modernization solutions while establishing quality assurance frameworks for AI-generated code, skills, agents, and prototypes. Develops test plans, acceptance criteria, regression suites, automated testing, security scans, and quality gates. Collaborates with DevOps teams on reliable multi-agent workflows, validates prototypes for production readiness, and monitors accuracy, security, compliance, and data-sensitivity risks.

Designs, builds, and operationalizes enterprise AI, generative AI, machine learning, and agentic AI solutions. Develops integrations, orchestration services, reusable tooling, deployment pipelines, monitoring, and governance controls. Partners with architecture, platform, data, and product teams to deliver scalable, secure, compliant AI capabilities across the organization.

Lead AI security strategy and guardrails, design and review AI gateways and tooling, scale InfoSec review processes, reduce AI-driven security noise, drive remediation and investigations, and mentor security and platform engineers.

Owns the end-to-end governance review and implementation of enterprise AI products. The role manages submissions through cross-functional Legal, Privacy, Security, Procurement, and technology reviews; coordinates blockers, schedules, documentation, and risk escalation; and leads approved solution onboarding across cloud, Okta, ServiceNow, CSDM, and enterprise technology teams. It also improves governance processes, develops implementation playbooks, and ensures compliant post-launch operations.

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.