Top Remote AI Engineer Jobs

The Data Engineer will develop and optimize data pipelines for LLMs, ensuring high-quality healthcare datasets while collaborating with data scientists on model requirements.

Design, build, and operate scalable data and AI platform capabilities. Create reusable abstractions, templates, and services for data pipelines and products. Integrate Generative AI for discovery and automation, ensure data quality, lineage, observability, security, and support cross-functional analytics and applied-AI teams.

Partner with Sales to design and demo AI-powered, data-driven solutions for life sciences clients using Komodo's Healthcare Map. Run live no-code analyses, build reusable demo assets, navigate AI governance and procurement, quantify ROI, mentor junior SEs, and feed product/AI roadmap input.

Build and maintain secure RESTful and event-driven APIs and MCP server implementations to allow AI agents to interact with enterprise platforms (ServiceNow, Okta, ERP). Implement authentication (OAuth2/OIDC), API gateway policies, audit logging, and monitoring. Collaborate with AI and governance teams, document integrations, and ensure secure, reliable, auditable agentic workflows.

The Data/AI Cloud Engineer will implement AWS data services, build ETL pipelines, optimize databases, and support data-driven solutions collaborating with technical teams.

The AI Automation Engineer will apply AI in finance operations to automate processes like AP, AR, and reconciliation, ensuring compliance and performance measurement.

Deploy and integrate AI-enabled SOC solutions across customer environments: design cloud-native ingestion pipelines, integrate SIEM/XDR/SOAR and tooling, implement automation and AI workflows, improve analyst experience, troubleshoot production issues, and provide technical leadership and field-facing delivery.

You will build and maintain AI-native application features, working with a team of AI and backend engineers to ensure quality and innovation.

The role involves architecting scalable backend systems for real-time evaluations, integrating with streaming data, and building intervention layers for AI applications. You'll design frameworks and operationalize vector databases while ensuring low-latency service performance.

Lead design and delivery of cloud-native, event-driven platforms and applications. Drive full lifecycle development, champion engineering best practices, implement production-ready solutions, and build AI/LLM capabilities like RAG, embeddings, and model CI/CD across multi-cloud environments.

Develop and implement compiler optimization algorithms for deep learning workloads, analyze networks, debug and profile GPU performance (CUDA), define public APIs, and deliver ahead-of-time and just-in-time compilation techniques for NVIDIA GPUs to improve inference performance, build times, and memory efficiency.

Build and productionize backend-first AI capabilities for a FinOps intelligence platform: design backend services, implement AI features (anomaly detection, recommendations, agents), create evaluation/rollback/monitoring systems, and collaborate with product to ship reliable, cost-efficient, production-ready features.

Embed with client engineering teams to operationalize LLMs and retrieval systems across multi-cloud environments. Build agentic solutions using Vertex AI Agent Builder and ADK, connect agents securely to BigQuery/Spanner via MCP, implement streaming pipelines (Dataflow) for real-time inference, and manage model training, tuning, deployment, and evaluation on Vertex AI. Provide consultative guidance, cross-functional collaboration, and clear stakeholder communication to ensure business value from Generative AI solutions.

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.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

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.

Lead design, training, and deployment of large-scale ML models (LLMs, diffusion, GNNs). Own end-to-end training pipelines, distributed training, evaluation, and inference architecture. Collaborate with data and infra teams, drive reproducible model development, and provide technical leadership and mentorship from research through production.

Design and build AI assistants for interactive 3D workflows: build NLP pipelines, integrate cloud and client LLMs, design knowledge graphs, fine-tune foundation models, prototype AI features, and improve personalization and system performance.

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

The role involves rapid prototyping and architecting production LLM pipelines for AI in marketing workflows. Responsibilities include developing proofs of concept, making AI architecture decisions, building production data agents, and understanding user workflows for seamless AI experiences.

As an Applied AI Engineer, you'll build AI agents and experiences from concept to production, collaborating across teams and optimizing systems for usability and quality.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

Lead design and implementation of scalable AI-centric systems for offensive security. Develop agentic AI capabilities for vulnerability identification and automated pentesting, own feature delivery from ideation to production, mentor engineers, run design/code reviews, and collaborate cross-functionally to integrate AI into platform systems.

Design and implement AI-powered educational software, focusing on generative AI systems, mentor peers, and ensure scalable, secure architecture.

Drive pre-sales for NICE Customer Service Automation by designing technical solutions, delivering demos, evaluating customer infrastructures, responding to RFIs/RFPs, and partnering with sales, product, and engineering to apply conversational AI, analytics, and digital engagement to CX problems.