Generative AI Engineer

Infinite Electronics International, Inc.

1d•Remote

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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
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
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
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