Top AI Engineer Jobs

Design, develop, and deploy generative AI, LLM-powered, and agentic systems for federal clients. Lead engineering best practices, implement secure integrations, enable cloud/DevSecOps pipelines, monitor production performance, and mentor engineers while collaborating across data, cloud, and stakeholder teams.

Develop, research, optimize, and deploy computer vision AI/ML algorithms for remote sensing, including automatic target recognition and multimodal EO/SAR data fusion. Responsibilities include processing large datasets, training supervised and unsupervised deep learning models, deploying solutions to cloud environments, reviewing research, documenting capabilities, and potentially leading junior engineers. The role requires an active TS/SCI clearance and supports defense and commercial applications.

Build and scale AI medical scribe features using LLMs, develop responsive web and iOS experiences, and maintain audio-processing, cloud, and EHR integration infrastructure. Lead projects from concept to production, collaborate with product and design teams, and work directly with customers to gather feedback and rapidly improve the product.

Develop, train, evaluate, deploy, and maintain computer vision and machine learning models for warfighter performance and visual defect or anomaly detection. Translate operational needs into maintainable ML solutions, design validation strategies, optimize model thresholds, and integrate inference into software systems. Collaborate with data engineers, data scientists, architects, subject matter experts, and product owners while communicating technical findings and tradeoffs.

Build and deploy production AI/ML features across the model lifecycle, including GenAI systems, RAG pipelines, data pipelines, evaluation infrastructure, and MLOps. Apply machine learning to manufacturing, supply chain, computer vision, predictive maintenance, forecasting, inventory, and logistics problems. Collaborate with engineering, manufacturing, and operations teams to deliver measurable outcomes.

Design and develop scalable AI platforms and developer tools using Python, MLOps, LLM prompting, and agentic workflows. Build ETL and data pipelines, integrate and deploy models on cloud infrastructure, implement monitoring and reliability dashboards, prototype generative AI applications, and establish testing, documentation, security, and ethical AI standards. Collaborate with data, engineering, and product teams to operationalize intelligent applications.

Leads hands-on design, development, and optimization of AI data movement and distributed storage systems. Responsibilities include integrating NVIDIA NIXL and DDN Infinia with GPU inference platforms, optimizing GPU-to-storage I/O using GPUDirect Storage, RDMA, and NVMe-over-Fabrics, developing KV cache and multi-tier storage strategies, benchmarking production systems, resolving performance bottlenecks, influencing distributed inference architecture, and mentoring engineers.

Lead the architecture and development of AI-powered engineering platforms, agentic development environments, inference infrastructure, and automated quality systems. Oversee GPU clusters, inference services, evaluation frameworks, testing strategies, and engineering guardrails. Guide technical direction, architecture, code reviews, platform productization, and Loop Engineering practices while mentoring engineers and partnering across software, AI research, platform, quality, and product teams.

Design, build, deploy, and operate secure, scalable AI-enabled products and internal tools. Evaluate models and services, develop prompts and context strategies, establish evaluations, monitoring, safeguards, and reusable architectures, and guide responsible AI adoption. Partner with product and engineering teams to prioritize opportunities, balance value and risk, mentor colleagues, and improve AI systems using production data and stakeholder feedback.

Designs and maintains the College’s enterprise data and AI platform. Develops integrations, governed data pipelines, warehouses, APIs, MCP services, dashboards, analytical applications, and AI-supported workflows. Establishes testing, monitoring, security, access controls, data governance, and evaluation standards. Partners with institutional offices to translate business needs into secure technical solutions while investigating unfamiliar systems, APIs, schemas, and workflows.

Supports deployment, configuration, maintenance, and troubleshooting of AI infrastructure, including servers, storage, networking, firmware, Kubernetes, and Slurm environments. Assists with hardware diagnostics, customer environment delivery, incident resolution, documentation, and operational support in secure environments while collaborating with engineering teams.

Own the architecture and hands-on development of Fanatics’ enterprise AI/ML platform, including the LLM gateway, agent and MCP runtime, retrieval infrastructure, evaluation systems, governance, and cost analytics. Set technical standards, make build-versus-buy decisions, deliver scalable platform capabilities, improve observability and security, mentor engineers, and communicate architectural trade-offs to senior leadership.

Build, secure, and maintain Azure and GCP cloud infrastructure across environments. Manage security controls, compliance, CI/CD, automation, monitoring, and deployments while integrating cloud services with software applications. Collaborate with engineering teams using AI-assisted development tools and LLMs. Support production software, AI agents, retrieval systems, and RAG applications. The role requires strong problem-solving, communication, collaboration, and independent delivery skills.

Architects and develops secure, scalable AI/ML systems and MLOps infrastructure across distributed cloud environments. Builds model training, inference, monitoring, governance, adversarial robustness, federated learning, and multi-agent safety capabilities. Designs agentic workflows, data and ETL pipelines, model lifecycle automation, and high-performance serving systems. Evaluates reliability and security under real-world and adversarial conditions, collaborates with cybersecurity and cloud teams, and mentors engineering teams on advanced machine learning and secure development practices.

Design, develop, optimize, and deploy scalable computer vision and vision-language systems using classical computer vision and deep learning. Build evaluation frameworks, monitor production models, debug issues, and improve architectures with new data. Collaborate with product managers and cross-functional teams to deliver practical AI solutions across cloud and edge environments.

Develop and optimize Azure SQL and SQL Server databases, including queries, stored procedures, indexing, triggers, data warehousing, and ETL. Build and maintain AI agents using autonomous and multi-agent architectures, prompt engineering, and RAG pipelines. Support Azure App Services and C# .NET applications, troubleshoot performance issues, lead database projects, mentor junior developers, and document technical solutions.

Build and deploy production machine learning applications, APIs, inference services, and data platforms. Engineer large-scale pipelines using distributed technologies, data modeling, ETL/ELT, transformation, and data quality practices. Translate defense and mission needs into scalable solutions across cloud, containerized, and GPU/CPU environments while collaborating with engineering, data, and mission teams.

Leads architecture, development, and delivery of AI-powered applications and enterprise data platforms. Designs Generative AI solutions using LLMs, agents, RAG, and Vertex AI; develops full-stack cloud-native applications; builds Databricks, Spark, and Lakehouse data architectures; and establishes GCP cloud standards. Provides technical leadership, mentoring, architecture guidance, governance, observability, CI/CD practices, technical evaluations, and technology roadmaps across engineering, data, analytics, and product teams.

Designs, develops, deploys, tests, monitors, troubleshoots, and maintains cloud and on-premise integrations using MuleSoft, Workato, or similar iPaaS platforms. Translates client requirements into integration and transformation solutions, supports production environments, documents implementations, leads SIT and UAT activities, manages change support, resolves integration issues, and mentors developers and architects.

Designs, deploys, and supports production AI/ML solutions across data ingestion, feature engineering, model training, MLOps, monitoring, and lifecycle management. Builds AWS cloud-native applications, generative AI systems, knowledge graphs, and real-time or batch pipelines. Partners with stakeholders on analytical solutions, ensures data quality and AI governance, leads technical design, and mentors engineers.

Partner with enterprise customers to identify AI use cases, architect and implement custom applications and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into actionable solutions, collaborate with Product and Engineering, improve technical integration processes, and drive customer adoption, time-to-value, and measurable business outcomes.

Partner with enterprise customers to identify AI use cases, architect and implement custom applications, templates, and integrations using WRITER’s platform, APIs, and Knowledge Graph. Translate complex AI concepts into solution recommendations, collaborate with Product and Engineering, develop scalable integration processes, and drive customer adoption, time-to-value, and business outcomes.

Partner with enterprise customers to identify AI use cases, validate feasibility, and implement tailored solutions using WRITER’s platform, APIs, and Knowledge Graph. Architect applications, templates, and integrations; explain AI capabilities to technical and executive audiences; collaborate with Product and Engineering; develop scalable integration processes and documentation; and drive customer adoption, expansion, and measurable business outcomes.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows. Own architecture from proposal through production, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement deployment, monitoring, logging, enterprise security, and reliability capabilities while mentoring engineers and delivering resilient production systems.

Develop and deploy large-scale computer vision and machine learning models for Coupang’s search and product experiences. Responsibilities include experimentation, statistical analysis, scalable model development and serving, A/B testing, researching advanced vision techniques, presenting results to leadership, and mentoring engineers. The role requires applying deep learning, transformers, generative modeling, and other state-of-the-art approaches to user-facing products.