Top AI Engineer Jobs

Design and build production-ready AI applications leveraging LLMs. Develop backend services orchestrating LLM interactions, prototype user interfaces, implement RAG and vector search, integrate enterprise data sources, monitor AI performance, apply prompt engineering, ensure responsible AI practices, and collaborate with cross-functional teams to deliver scalable AI-enabled solutions.

Develop electro-optical architectures and optimize integrated electro-photonic components for next-generation optical interconnects. Lead cross-functional R&D, create simulation methodologies and mixed-signal/digital accelerators, advise post-silicon test plans, and document designs for scalable electro-photonic systems.

Design, train, test, deploy, and maintain end-to-end AI/ML systems for production. Select algorithms, architect MLOps pipelines, optimize models for high-throughput text/image/video feeds, and collaborate with engineers and product owners to deliver mission-critical solutions for federal and defense clients.

Lead operations for large-scale AI infrastructure: manage NVIDIA GPU and high-performance networking environments, troubleshoot Linux/Kubernetes/storage/hardware issues, drive incident response and root cause analysis, improve observability and automation, mentor engineers, and collaborate with engineering and datacenter teams to ensure platform reliability and scalability.

Lead architect and hands-on builder for AI solutions: evaluate feasibility and data readiness, translate business problems into technical and algorithmic requirements, design high-level architectures and prototypes, drive PoC to production handover, set MLOps/API standards, mentor team, and align North American implementations with European enterprise governance.

Build and maintain Python data pipelines and integrations to make enterprise data discoverable. Develop metadata enrichment, semantic knowledge layers, vector embeddings, and search/index tuning. Maintain Databricks/AWS Glue pipelines, sync taxonomies/ontologies, build APIs/MCP servers, and troubleshoot data quality and pipeline issues to support retrieval-augmented generation and AI-powered discovery.

Lead design and development of enterprise software solutions using Java and Cursor AI. Provide technical leadership, mentor engineers, define architecture and roadmaps, implement cloud-native, microservices, CI/CD and infrastructure-as-code practices, and collaborate across product and engineering teams to deliver performant, secure systems.

Own operational readiness and productionization for internal AI-enabled tools: build and maintain CI/CD and IaC, support Vercel and multicloud deployments, improve monitoring, secrets and access controls, runbooks, and lifecycle standards. Partner with Corporate AI, IT, Security, and business teams to move prototypes to production and ensure reliability, security, and maintainability.

Design, build, and operate a LangGraph-based multi-agent orchestration platform for production AI: agent integration contracts, stateful multi-turn context management, guardrails for PHI/clinical safety, RAG retrieval pipelines (Azure AI Search), versioned prompt architecture, evaluation/regression suites, and integrations to commercial and internal agents. Collaborate with cross-functional teams to implement, test, and maintain agentic systems in a regulated domain.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Build and ship production LLM workflows for contract review, redlining, extraction, and legal Q&A. Design agent orchestration and multi-step reasoning pipelines, diagnose and fix model failures, work with customers to turn legal problems into reliable AI systems, and own features end-to-end from design to production.

The Senior AI Engineer will architect LLM agents, drive ML lifecycle processes, build GenAI solutions, and lead a data engineering team. Responsibilities include overseeing data architecture, scalable deployments, and mentoring.

The Lead AI Engineer will build LLM-powered features, optimize AI infrastructure, and lead the execution of AI projects. Responsibilities include designing pipelines, evaluating models, and collaborating with product teams.

The Founding AI Engineer will develop an end-to-end scan-to-BIM pipeline, focusing on 3D scene understanding and BIM generation from spatial data.

As a founding ML research engineer, you'll design AI-powered engineering systems, implement ML algorithms, and integrate deep learning techniques while collaborating with experts in the field.

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.

Lead design and delivery of GenAI and BI data platform capabilities, driving data governance, metadata/lineage, pipeline engineering, tenant onboarding, platform adoption, and compliance across enterprise systems while mentoring engineers and partnering with stakeholders.

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.

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.

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 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.

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.