Applied AI Engineer
Applied AI Engineers build intelligent features into products by integrating LLMs, retrieval systems, and AI APIs to solve real business problems. Day-to-day, they prototype and productionize AI-powered workflows—from designing agent architectures and evaluation frameworks to implementing retrieval pipelines and optimizing inference costs at scale. They sit between product and infrastructure teams, combining hands-on engineering with deep customer collaboration to ship features that work reliably in production. Unlike ML Engineers who train models or Forward Deployed Engineers who embed at customer sites, Applied AI Engineers own the full stack of AI integration within their own organization's products, from architecture decisions to code contributions and technical mentorship.
Skills
What companies are looking for in this role.
Designing and architecting scalable AI systems and applications from concept to production deployment
Building and optimizing APIs, microservices, and backend services for AI model integration and inference
Translating complex research and business requirements into production-ready systems
Developing evaluation frameworks, benchmarks, and monitoring systems for AI model performance
Optimizing model performance, latency, throughput, and resource efficiency
Building and maintaining data pipelines, schemas, and data architecture for AI systems
Creating reusable infrastructure, patterns, and components for team-wide adoption
Profiling, debugging, and optimizing performance-critical code for CPU, memory, and latency
Implementing enterprise integrations with strong authentication, permissions, and audit trails
Bridging Python with lower-level languages for performance-critical components
Building responsible and ethical AI systems with bias detection and safety alignment
Designing and implementing idempotent, retry-safe, and rate-limit-handling systems
Working with constrained computational environments and resource optimization
Integrating large language models and embeddings into production applications
Implementing agentic workflows, multi-agent orchestration, and tool routing systems
Prototyping and experimenting with new AI features and capabilities rapidly
Building internal automation solutions and workflow orchestration systems
Working with RAG systems, context engineering, and retrieval-based architectures
Understanding and applying structured outputs and tool use for AI model control
Collaborating across cross-functional teams including product, research, and infrastructure
Communicating technical decisions, proposals, and progress to non-technical stakeholders
Navigating ambiguous problem spaces and operating with limited oversight in early-stage environments
Designing product features end-to-end with customer discovery and user feedback
Measuring and defining success metrics for AI-powered features and workflows
Technology
The tools and technologies that define this role.
Open Jobs
83 open Applied AI Engineer jobs across 42 companies.
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