Generative AI Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Generative AI Engineer based in United States. This role offers the opportunity to design, build, and operate advanced generative AI solutions that deliver measurable business impact. You will take ownership of production-grade AI systems, from architecture and implementation through deployment and continuous optimization. The position focuses on cutting-edge technologies including LLM applications, agentic workflows, retrieval-augmented generation (RAG), and enterprise AI integrations. You will help define engineering standards, establish best practices, and shape the future of AI development within a collaborative technical environment. Working across architecture, security, governance, and operations, you will ensure AI solutions are scalable, reliable, and responsible. This is an ideal opportunity for an experienced AI engineer who enjoys solving complex problems and driving innovation through practical applications of emerging technologies. Accountabilities The Generative AI Engineer will own the technical delivery and evolution of enterprise AI solutions, ensuring systems are designed for reliability, scalability, security, and continuous improvement. The role combines hands-on engineering with technical leadership, requiring close collaboration with engineering teams and business stakeholders. • Design and develop production-grade generative AI systems, including LLM-powered applications, agentic workflows, and multi-step RAG architectures integrated with enterprise data and services. • Establish reusable engineering patterns for prompt management, workflow versioning, structured outputs, tool orchestration, and AI service lifecycle management. • Evaluate and optimize AI model selection, routing strategies, latency, token usage, cost efficiency, and application architecture decisions. • Integrate AI solutions with enterprise platforms, APIs, databases, and cloud-native services to create reliable production workflows. • Own operational performance of AI systems, including reliability, monitoring, observability, scalability, and incident response. • Build and maintain CI/CD pipelines supporting AI deployment, versioning, testing, and release management. • Develop automated evaluation frameworks for LLM outputs, including prompt regression testing, retrieval quality validation, and failure analysis. • Implement responsible AI practices through security controls, content guardrails, human-in-the-loop processes, and structured output validation. • Define technical standards and best practices for generative AI development across teams. • Provide technical guidance through architecture reviews, code reviews, mentorship, and collaboration with engineering stakeholders. • Promote iterative delivery practices by continuously improving AI solutions based on feedback, performance data, and evolving technology trends. Requirements The ideal candidate brings deep experience building and operating production AI systems, with strong software engineering fundamentals and expertise in modern generative AI architectures. They should be comfortable leading technical decisions while collaborating across multidisciplinary teams. • Demonstrated experience delivering production-grade LLM or generative AI systems, including prompt engineering, workflow design, model selection, agent orchestration, and AI application architecture. • Strong experience building automated evaluation pipelines for LLM applications, including retrieval validation, regression testing, and failure mode analysis. • Experience implementing enterprise AI safeguards such as human review workflows, content filtering, schema validation, and security controls. • Proven background designing and operating distributed systems in enterprise production environments with ownership of reliability, performance, and scalability. • Experience developing and managing CI/CD pipelines for AI services and cloud-based applications. • Ability to define and enforce GenAI engineering standards, architecture patterns, and development best practices. • Experience building cloud-native APIs, microservices, and event-driven architectures using Azure or comparable cloud platforms. • Experience integrating AI systems with enterprise data sources, internal APIs, and security frameworks. • Strong programming skills in Python and experience with AI development frameworks and production engineering practices. • Familiarity with technologies such as Azure OpenAI, AI Foundry, App Service, Functions, Service Bus, Blob Storage, Key Vault, Application Insights, or similar cloud services is preferred. • Experience with agentic AI systems, RAG architectures, retrieval optimization, chunking strategies, grounding techniques, and ranking approaches is a plus. • Knowledge of Docker, containerized workloads, infrastructure-as-code practices, and modern