Top AI Engineer Jobs

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.

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.

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 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.

Develop and integrate locally hosted LLM and ML capabilities into accredited, air-gapped government applications. Build data pipelines, REST API integrations, logging/audit instrumentation, and support deployment and monitoring in classified environments. Produce testable, documented code and collaborate with program stakeholders while following security and AI governance requirements.

Design, build, and sustain AI analytics stack components including Python backends, FastAPI services, LLM-enabled agentic workflows, and Streamlit/React frontends. Implement CI/CD and Docker-based deployments, integrate relational databases (Postgres/MySQL), and ensure secure, maintainable delivery. Collaborate cross-functionally to prototype, deploy, and operate AI-enabled user interfaces and APIs to support mission outcomes in a cleared, on-site environment.

Lead QA strategy for large-scale Generative AI products and platforms. Design scalable testing and automation across AI pipelines, APIs, UIs, microservices, and SaaS. Establish release-readiness gates, diagnose model degradation and system failures, drive root-cause analysis, and mentor QA engineers while collaborating with Product, Engineering, DevOps, UX, and Customer Engineering.

Own and ship end-to-end production features using agentic development. Build and operate multi-agent workflows, validate agent output, work in brownfield and greenfield environments, pair with engineers to spread agentic practices, and maintain long-term quality and reliability of systems.

Design, prototype, and productionize AI-enabled software in cloud environments (Azure). Collaborate with clinical and business users, iterate on prototypes, maintain code quality, contribute to data pipelines and infrastructure, and follow data governance and PHI security requirements.

Build and operate cloud infrastructure for an AI platform: design Terraform modules, run production Kubernetes, manage CI/CD and release engineering, implement observability and SLOs, enforce security/compliance controls, manage secrets and certificates, and support incident response and platform readiness.

Design, build, and operate scalable data pipelines and ETL for AI/ML. Create model-ready datasets (features, embeddings, prompts), implement storage layers (data lakehouse, vector stores), optimize low-latency retrieval for inference and RAG, tune search/indexing with ElasticSearch/OpenSearch, collaborate with analytics and product teams, and maintain CI/CD, version control, and Linux-based performance tuning.

Design and build agentic AI systems and internal coding/research agents; architect and analyze inference workload dynamics; collaborate with evaluation teams to improve software and hardware evaluation quality.

Design, build, and operate scalable, cloud-native AI inference platforms for production ML workloads. Optimize GPU utilization and inference performance, implement autoscaling, monitoring, security, and collaborate with ML teams to deploy and support enterprise AI models.