Generative AI Engineer

Position Description

We are seeking a Generative AI Engineer to own the hands-on technical delivery of production GenAI systems - from architecture and implementation through deployment, operations, and continuous improvement. This role covers the full stack of modern GenAI engineering: LLM application design, agentic and RAG workflows, structured output patterns, evaluation pipelines, and operational safeguards, integrated with enterprise data sources and cloud-native services.

Beyond building, this person helps to define the technical standard for GenAI work on the team - establishing engineering patterns, owning architectural decisions, and serving as the primary authority on GenAI best practices and tooling. The right candidate brings deep, demonstrable production GenAI experience, a strong sense of operational ownership, and the technical credibility to lead by example.

General Duties and Responsibilities

AI Architecture & Delivery

• Design and build production-grade generative AI systems - agentic workflows, multi-step RAG pipelines, and LLM-powered applications integrated with enterprise data and services
• Define and implement reusable engineering patterns for prompt management, workflow versioning, structured outputs, tool orchestration, and rollback across production AI services
• Apply judgment around model selection and routing, token and latency optimization, cost management, and the appropriate boundaries between AI-driven and deterministic application logic
• Continuously evaluate emerging AI models, tools, and architectural approaches, incorporating improvements into existing systems incrementally
• Integrate AI systems with enterprise data sources, internal APIs, and platforms to enable reliable, production-ready workflows

Reliability, performance & operations

• Own operational outcomes for production AI systems - reliability, latency, throughput, cost efficiency, and scalability targets
• Implement and maintain monitoring, observability, tracing, and alerting frameworks to ensure operational visibility and rapid issue resolution
• Design and maintain CI/CD pipelines for deployment, versioning, and release management of AI services
• Lead production incident response and root cause analysis, driving systemic improvements that reduce recurrence

Governance, security & responsible AI

• Build and maintain automated evaluation pipelines for LLM outputs - prompt regression testing, retrieval quality validation, and failure mode tracking
• Implement human-in-the-loop controls, content guardrails, schema validation, and structured output enforcement to ensure trusted and auditable AI outputs
• Secure AI systems against prompt injection, data leakage, and unauthorized access, aligning with enterprise compliance and security standards

Technical authority & collaboration

• Own the team’s GenAI technical direction - defining and enforcing engineering standards, patterns, and best practices across all GenAI workstreams
• Make and defend architectural decisions with clarity, providing the technical rationale needed for the Manager and stakeholders to align and move forward confidently
• Work closely with the Manager, GenAI Engineering to receive, refine, and execute on scoped GenAI work - contributing technical judgment to prioritization and tradeoff decisions
• Provide hands-on code review and technical guidance to engineers contributing to GenAI workstreams, raising overall quality through direct feedback and demonstration
• Champion an iterative delivery culture - shipping incrementally, incorporating feedback, and improving continuously in a regular production release cadence

Education and/or Experience

Required Experience

• Demonstrated experience shipping production-grade LLM or generative AI systems - prompt and workflow design tradeoffs, model selection and routing decisions, tool use and agent orchestration boundaries, and the distinction between AI guardrails and deterministic application logic
• Experience building automated evaluation pipelines for LLM outputs, including gold set construction, model-based evaluation approaches, prompt regression testing, retrieval quality validation, and failure mode analysis across the full LLM application stack
• Experience implementing human-in-the-loop controls, content guardrails, and schema-based output validation for enterprise AI deployments
• Strong track record designing, building, and operating complex distributed sy…