Top Remote AI Engineer Jobs

Build and maintain full-stack product features, backend services, APIs, data pipelines, and AI integrations to transform customer interaction data into AI-powered experiences. Collaborate with cross-functional teams to design, test, and support production systems, internal tools, and user-facing interfaces. Own ambiguous problems end-to-end and raise engineering quality across the data and AI platform.

Embedded with strategic customers, own end-to-end design, build, and production rollout of cloud infrastructure for large-scale GPU/HPC AI workloads. Build compute orchestration, platform services, onboarding sandboxes, and reliability/security tooling. Turn field learnings into reusable platform capabilities and partner with Product and Engineering to productize them.

Design, develop, and maintain microservices-based, AI-enabled software for safety-critical air-traffic systems. Work in Agile/SAFe teams to implement CI/CD/DevSecOps practices, integrate with databases, messaging, and external APIs, use AI-assisted tools, and support testing, performance tuning, and reliable delivery while meeting Public Trust and U.S. citizenship requirements.

Design, implement, and operate a private, secure AI platform and autonomous agent workforce for engineering workflows. Build agents (FastAPI/CLI/chat), integrate MCP and RAG, maintain containerized infrastructure, CI/CD validation, secrets and cost management, and enterprise integrations (GitHub, Jira, Confluence, Teams). Continuously evaluate models, improve agent reliability, and drive platform adoption across low-level systems engineering domains.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Lead the development and operation of a repeatable risk review practice across cloud, application, vendor, identity, and emerging AI systems. Perform threat modeling and risk reviews, define secure-by-default standards and controls, embed risk into procurement and delivery, mentor engineers, and drive cross-organizational governance to improve risk posture and program maturity.

Deploy and optimize PerfectServe's AI voice agent for customers: configure rollouts, build evaluation suites, tune prompts, reduce latency and inference cost, ensure PHI/security compliance, run model benchmarking and observability, debug production issues, and automate onboarding with Product and Customer Success.

Deploy, configure, and optimize an AI voice agent for healthcare customers. Build evaluation suites, tune prompts, reduce latency and inference cost, monitor security/PHI handling, and automate onboarding while partnering with Product and Customer Success for go-live and production support.

Develop and maintain an AI-driven pipeline to auto-generate, process, and publish real-life and digital quests. Integrate AI frameworks, design scalable automation workflows, analyze user data to improve generation quality, build API integrations, participate in architecture and documentation, test with pytest/unittest, and collaborate with cross-functional teams to validate LLM outputs against real-world data.

Lead end-to-end GPU-focused Kubernetes deployments from bare metal to production-ready vCluster environments. Configure and troubleshoot GPU nodes (GPU Operator, CUDA, RDMA/InfiniBand), CNI and storage (CSI, Ceph/Rook/Weka/Longhorn), validate managed K8s, produce playbooks, transfer knowledge to customers, and feed infrastructure learnings back to Product and Engineering while supporting pre-sales proofs of value.

Lead rollout and enablement of an agentic AI platform across departments: deploy and integrate generative AI tools, build cost and usage dashboards, define vendor risk controls, create operational SOPs and training, and bridge IT, security, and engineering to scale safe AI workflows.

Build and own core backend infrastructure for long-running agentic AI workflows, ensuring high reliability, observability, disaster recovery, secure execution sandboxes, HIPAA-compliant data handling, and production-grade IAM and secrets management.

Build and operate secure, scalable ML pipelines and hybrid GPU infrastructure for training, fine-tuning, serving, and governing open-source LLMs and agentic apps. Implement CI/CD, model versioning, observability, benchmarking, security controls, and integration with RAG/agent tools and vector DBs to support production deployments across tenants.

Design, train, evaluate, and optimize agent-native LLMs and RAG pipelines for enterprise use. Build training and RL pipelines (RLHF/DPO/PPO), embedding-based memory, evaluation harnesses, observability, and inference optimization across cloud and on-prem environments.

Design behavioral specs, harnesses, and validation systems so AI agents implement reliable full-stack features. Build CI gates, structural tests, linting, knowledge bases, and end-to-end verification. Diagnose failures across spec, harness, and agent layers. Architect and operate production systems across cloud and on-prem environments for regulated energy deployments, prioritizing reliability, observability, and graceful degradation.

As a Senior Fullstack Software Engineer, you will design and build fullstack systems for AI agents, collaborating with engineering, product, and design teams while documenting findings and participating in technical discussions.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

Design and build secure, production-grade infrastructure that enables agentic AI systems to interact with Strikes platforms. Develop authentication/authorization for agents, orchestration and runtime environments, IDE agents, enterprise knowledge connections, tiered autonomy safety mechanisms, and embed with teams to operationalize frontier AI tools while enforcing security and ownership.

The Principal Engineer will lead AI-driven productivity initiatives, develop a strategy for integrating AI in engineering workflows, and improve collaboration across teams. Responsibilities include executing projects, ensuring code quality, and setting success metrics.

Design and implement backend services for an AI-driven automation platform, focusing on LLM orchestration, API development, and system reliability.

Design and build applications using Python, manage component design, optimize performance, and collaborate with clients on innovative products.

Design and build a production multi-agent harness and MCP integrations that operate across ERPs, CRMs, and business tools. Own end-to-end production concerns (reliability, observability, permissions, safe workflow execution), scope the product, make architecture decisions, and ship features working closely with CTO, product, engineering, and design. Stay current with agent frameworks and use tools like Claude Code in development.

Build and maintain full-stack web and mobile-friendly applications, design and manage Airtable backends, implement workflow automations (n8n/Zapier), integrate LLM/AI services (e.g., Anthropic Claude), optimize performance, collaborate with leadership and vendors, and document systems to reduce single-owner risk.

Design, build, and operate end-to-end data and AI systems: scalable ETL pipelines, model development and deployment, backend services/APIs, cloud infrastructure, MLOps, data governance, and lead technical reviews while mentoring engineers and collaborating with stakeholders.