Top AI Engineer Jobs

Build a scalable platform around foundation models and LLMs, architecting multi-step, stateful workflows and high-performance infrastructure to manage asynchronous, parallel queries and enable autonomous research.

Client-facing AI engineer who develops and deploys generative AI/LLM solutions, collaborates with data scientists and engineers, leads cross-functional projects, and delivers technical solutions and presentations to clients.

Client-facing AI Engineer who develops and deploys AI and generative AI solutions, collaborates across data science and engineering teams, leads cross-functional projects, and delivers technical solutions using analytics and BI tools to drive client outcomes.

The role involves conducting research and developing generative AI technologies, creating prototypes, collaborating with partners, and driving innovation at American Express.

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 Technical Lead will design, build, and modernize IVR and Voice Bot platforms, providing technical ownership and leadership in a hands-on role within the Contact Center Technology department.

As an AI Engineer Intern, you'll assist with building AI solutions, testing ML techniques, managing datasets, and contributing to production AI systems while gaining hands-on experience in a collaborative environment.

The AI Engineer Intern will assist in building and testing AI/ML solutions, contribute to production AI systems, and explore agentic AI frameworks, gaining hands-on experience in a dynamic environment.

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.

Build reference implementations, demos, and tooling for You.coms AI search and retrieval APIs; contribute and maintain open-source projects; engage and support the developer community; reduce inference cost and improve model performance; represent the company at events and translate technical work to business stakeholders.

The Senior Data & AI Engineer will design, build, and deploy complex data solutions, focusing on AI and data management. Responsibilities include creating data pipelines, enhancing data quality, and supporting analytics and machine learning initiatives.

As a Software Engineer at Paces, you will build AI automation solutions to expedite power development, working closely with industry experts and taking ownership of projects to enhance scalability and features.

Design and develop large-scale multimodal transformer models that fuse camera, LiDAR, and radar. Research cross-modal attention and token-fusion strategies, build scalable distributed training pipelines, explore self-supervised/contrastive pretraining, and optimize models for real-time, low-latency inference.

The Senior AI Engineer will design and deliver AI-driven software solutions, enhance AI tool use, and collaborate across teams to meet business goals.

As an AI Engineer, you will develop, train, and deploy machine learning models, collaborate with stakeholders, and contribute to ML strategies and solutions for Global Defense.

The role involves designing intelligent agent architectures and developing multi-agent systems, integrating unstructured data with Knowledge Graphs, and fine-tuning models for edge deployment.

The AI Engineer will develop and deploy GenAI applications, collaborate with teams, and present at conferences, leveraging strong technical skills and industry experience.

As a Principal Machine Learning Engineer, you'll design and implement core ML systems for AI agents, collaborating with cross-functional teams and overseeing the full lifecycle of products from data strategy to deployment.

The AI Engineer will design, build, and deploy advanced AI systems, focusing on Large Language Models and agentic AI solutions within the Vantage platform, while collaborating with teams to meet user needs in an Agile environment.

Own end-to-end voice platform migration and scaling: LiveKit migration, real-time turn-taking, VAD, SIP/WebRTC, multi-agent handoff, training/fine-tuning voice models, inference cost optimization, and retrieval-based grounding using Supabase/pgvector.

Design, build, and operate production-grade AI/ML systems and agentic applications end-to-end. Implement data pipelines, model development, RAG and retrieval, observability, performance optimization, and CI/CD deployments in cloud-native environments. Develop multi-agent systems, APIs, and scalable services while ensuring monitoring, latency optimization, and security clearance eligibility.

Develop and maintain XR products and spatial UIs to interface with humanoid robots and data-capture hardware. Implement features, fix bugs, harden XR data sync, integrate custom hardware with XR headsets, and support hardware experiments, low-latency video streaming, and motion-capture workflows.

Design, implement, and train reinforcement learning and multi-agent algorithms; build scalable Python-based training pipelines and simulated environments; run distributed training with cloud and container orchestration; transition models to production, optimize system performance, and collaborate with domain experts to align rewards and evaluations with mission objectives.

Design, build, and deploy agentic AI systems and multi-agent workflows to detect anomalies, fraud, bot attacks, API abuse and network threats using large-scale telemetry, RAG, knowledge graphs, model monitoring, and security integrations. Provide technical leadership, mentor junior engineers, and define standards for AI agent development.

Embed with clients to discover pain points and build production-grade AI agents, RAG and automation workflows integrating with CRMs/ERPs. Own delivery lifecycle, train client teams, and codify reusable components across engagements.