Top Remote AI Engineer Jobs

Lead end-to-end delivery of AI-powered applications: define requirements with clients, translate business needs into technical plans, coordinate cross-functional engineering teams, leverage AI tools for prototyping and delivery, track progress, remove blockers, and drive rapid iteration and continuous improvement.

Deliver and harden cross-product AI solutions within the OneStream ecosystem. Lead customer-facing AI implementations, create reusable assets and documentation, troubleshoot field issues, collaborate with Delivery and Engineering, and engage finance and data stakeholders to drive production-ready AI outcomes.

Lead design and delivery of API and data engineering solutions: build end-to-end data pipelines, develop ontologies and schemas, ensure data quality and governance, integrate systems (including cloud/GCP), enable AI capabilities, enforce security/compliance, and mentor/lead technical teams within an Agile consulting environment.

Embed with Finance to design, build, and operate production AI systems that integrate with ERP and data platforms, enable conversational and retrieval-based workflows, automate financial quality controls, surface operational intelligence, and support executive reporting. Own end-to-end delivery from discovery through deployment, monitoring, evaluation, and enablement while ensuring governance, compliance, and stakeholder adoption.

Own and scale the Nx-based monorepo, build system, and CI/CD to make builds, tests, and developer workflows fast, reliable, and observable. Build developer tooling, local dev stacks, and production-grade developer infra with SLOs, on-call, and measurable DX improvements.

The Applied AI & Optimization Engineer will develop optimization algorithms, implement analytics layers, and integrate AI assistants within the manufacturing platform.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.

Lead a cross-functional engineering team with hands-on architecture and coding. Mentor engineers, drive technical execution, improve delivery practices, and collaborate with Product and QA to deliver reliable, modern cloud-based AI platform solutions.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.

Lead design and orchestration of AI-first agent workflows to produce application code, infrastructure, and tests. Architect full-stack systems and MCP servers, validate AI-generated outputs for correctness, security, and performance, build verification pipelines and observability, mentor teams, and evolve AI-First SDLC and governance practices.

Design and own AI-agent-driven test workflows and the automated verification framework. Build test context, select models and tooling, validate AI-generated code and tests, operate test automation across UI/API/mobile/data, ensure security and compliance, mentor teams, and continuously improve agent orchestration and observability.

Lead a small AI-First engineering team to design, orchestrate, and verify agent-driven development workflows. Define agent toolchains, context engineering, and governance; architect full-stack applications; enforce multi-layer verification and automated QA; mentor engineers; optimize token/cost; and continuously improve AI-assisted SDLC practices and standards.

Deploy, customize, and operationalize Outreach's AI Revenue Agents in customer environments. Map GTM workflows, integrate SaaS/CRM data, tune agent behavior, build reusable implementation patterns, liaise with Product/Engineering, and train customer teams to drive measurable revenue outcomes.

Design and implement runtime protocols, formats, and systems to capture, serialize, stream, and replay interactive 3D content across engines and AI models. Own engine subsystems from low-level C++ runtime to high-level C# APIs, optimize for performance, determinism, and low-latency streaming, and build interfaces for AI systems to inspect and invoke engine capabilities.

Design and implement runtime protocols, formats, and systems to capture, serialize, stream, and replay interactive 3D content across engines. Own engine subsystems from low-level C++ runtime to high-level C# APIs. Optimize for low latency, bandwidth, determinism, and memory; integrate AI systems to inspect and control engine state.

Lead design and implementation of agentic AI/ML solutions and modern software (APIs, microservices, CI/CD, streaming) on cloud platforms. Bridge legacy IBMi AS400 systems with modern AI, train and fine-tune models, oversee lifecycle, mentor engineers, set technical direction, and improve automation and testing across claims adjudication systems.

Work across AI, backend, and frontend: curate and clean writing datasets, fine-tune large language models, run training experiments, develop metrics and eval sets, build backend services in Python/Django, and implement frontend features with React.

Lead end-to-end customer engagements to discover, scope, deploy, and realize value from AI agents. Run executive workshops, design and configure agentic solutions, create deployment playbooks, partner on integrations, and drive measurable business impact while shaping product direction.

Design, implement, and evaluate novel AI systems (LLMs, automated reasoning, knowledge acquisition) for assessing journalism. Translate research ideas into scalable prototypes, run experiments, adapt/fine-tune models, develop new algorithms and evaluation methods, and communicate results via reports and papers.

Build and operate the AI platform and runtime infrastructure for LLM/agent-powered products. Deliver CI/CD pipelines, model serving (self-hosted and managed), eval frameworks, key management, observability, cost controls, and developer tooling. Partner with AI, software, security, and finance teams and produce runbooks, docs, and best practices to ensure reliable, secure, and cost-effective AI deployments.

Design, build, and deliver AI-driven Salesforce Agentforce agents and intelligent automations. Develop integrations, test automations, debug Apex/JavaScript/Python code, produce technical documentation, mentor junior engineers, participate in DevOps and QA, and support transformational projects and high-priority incidents.

Build, maintain, and optimize cloud data pipelines and dbt models; design Airflow DAGs; implement dimensional models and BI dashboards; integrate healthcare data with HIPAA-compliant practices; contribute to RAG pipelines and MLOps workflows; review contractor code, monitor data quality, and document designs following governance standards.

This role involves designing, developing, testing, and maintaining software solutions, collaborating with various teams while focusing on scalable and reliable engineering.

Lead design and build of AI-driven enterprise contact center and CX solutions in pre-sales: translate workflows into prototype architectures, architect retrieval/orchestration/validation systems, run technical discovery, partner with account teams, and deliver production-grade proofs of concept that handle real-world constraints and KPIs.

The role involves leading the architecture, development, and scalability of fullstack systems for AI agents while mentoring engineers and making technical decisions.