Position DescriptionWe 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 ResponsibilitiesAI 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 ExperienceRequired 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 systems in enterprise production environments, with clear ownership of reliability, performance, and operational outcomes
- Experience with CI/CD pipeline design and operation for AI services - including deployment strategies, versioning, and release management in production environments
- Proven ability to define and enforce GenAI engineering standards, patterns, and best practices across a cross-functional team
- Experience designing and operating cloud-native APIs, microservices, and event-driven architectures on Azure or equivalent cloud platform
- Experience integrating AI systems with enterprise data sources, internal APIs, and security controls in compliance-sensitive environments
- Demonstrated track record of shipping production AI systems iteratively - with regular release cadence, feedback incorporation, and continuous improvement
- Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience
Preferred Experience- Experience designing and operating agentic AI systems and multi-step RAG architectures in production - retrieval quality optimization, chunking strategies, grounding, and ranking tradeoffs
- Hands-on experience with Azure OpenAI, AI Foundry, App Service, Functions, Service Bus, Blob Storage, Key Vault, and Application Insights; familiarity with Bicep for IaC
- Experience with Python frameworks commonly used in production AI services, including FastAPI, asyncio, and Pydantic
- Familiarity with PySpark notebooks for data pipeline development
- Experience deploying and managing containerized AI workloads using Docker or similar technologies
- Familiarity with responsible AI principles, AI governance frameworks, and regulatory considerations relevant to enterprise AI systems
- Familiarity with Bronze/Silver/Gold medallion architecture and staged data quality patterns for enterprise data pipelines
- Domain experience in product data, PIM, ERP, master data management, data governance, ecommerce, or analytics platforms
- Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
Physical Job Requirements - Prolonged periods in a stationary position at a desk and working on a computer.
- Must be able to communicate effectively via video conferencing, phone, and written correspondence.
- Occasional travel may be required depending on project or business needs.
Work EnvironmentThe work environment is typically in a remote office setting during normal or extended business hours.
AccommodationCandidates for the position should be able to perform essential job duties in described work environment with or without accommodation. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
