Manager of Generative AI Engineering

Infinite Electronics International, Inc.

3d•Remote

About The Position

We are seeking a Manager of GenAI Engineering who splits their time equally between hands-on technical work and people leadership. As a practitioner, you will design and build production GenAI systems, translate stakeholder requests into engineering work, and upskill the team through active demonstration. As a leader, you will manage a team of 7-10 engineers, run delivery cycles, and drive execution quality. The ideal candidate brings proven GenAI depth, a demonstrated track record of growing engineers around them, and the leadership experience to manage a team without losing their technical edge. This role suits a strong technical leader ready to take the next step, whether from a prior management role, team lead, or senior IC background. Domain experience in product data, PIM, ERP, or ecommerce is a plus; GenAI depth and leadership effectiveness are the primary criteria.

Requirements

Demonstrated experience shipping production-grade LLM or generative AI systems - prompt and workflow design tradeoffs, model selection and routing, tool use and agent orchestration, 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, prompt regression testing, retrieval quality validation, and failure mode analysis
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
Significant hands-on technical experience with a career primarily rooted in software engineering, anda track recordof growing into or toward technical leadership - whether as a prior manager, team lead, or senior IC ready to take the next step
Experience establishing engineering standards, influencing architecture decisions, and raising technical quality across distributed or cross-functional teams
Experience mentoring or upskilling engineers on GenAI concepts and practices in a production context
Proven ability to translate ambiguous stakeholder requests into well-scoped, actionable engineering work
Iterative delivery mindset - ships incrementally, incorporates feedback, and drives continuous improvement
Experience integrating AI systems with enterprise data sources, internal APIs, and security controls in compliance-sensitive environments
Bachelor's degree in Computer Science, Engineering, Data Science, or related field, or equivalent practical experience

Nice To Haves

Domain experience in product data, PIM, ERP, master data management, data governance, ecommerce, or analytics platforms
Familiarity with Bronze/Silver/Gold medallion architecture and staged data quality patterns for enterprise data pipelines
Experience designing and operating agentic AI systems and multi-step RAG architectures in production - retrieval quality optimization, chunking strategies, grounding, and ranking tradeoffs
Experience designing and operating cloud-native APIs, microservices, and event-driven architectures on Azure or equivalent cloud platform
Familiarity with responsible AI principles, AI governance frameworks, and regulatory considerations relevant to enterprise AI systems
Hands-on experience with Azure OpenAI, AI Foundry, or equivalent AI platform services
Experience with Python frameworks commonly used in production AI services, includingFastAPI,asyncio, andPydantic
Familiarity with PySpark notebooks for data pipeline development
Experience deploying and managing containerized AI workloads using Docker or similar technologies
Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field

Responsibilities

Lead, coach, and develop a team of 7-10 engineers - setting clear expectations, running performance conversations, and building a culture of ownership and continuous improvement
Actively upskill engineers on GenAI concepts, patterns, and practices through pairing, code review, and hands-on demonstration
Translate incoming GenAI requests from product, business, and platform stakeholders into well-scoped engineering work - surfacing tradeoffs and setting clear acceptance criteria
Hire and onboard strong engineers; build team capacity alongside capability
Run iterative delivery cycles - sprint planning, backlog grooming, dependency management, and blocker removal
Own operational outcomes for production AI systems - reliability, latency, throughput, cost efficiency, and scalability targets
Partner with the Sr. Manager and cross-functional stakeholders to align engineering execution with the broader product data and AI strategy
Represent engineering in planning discussions; communicate status, risks, and tradeoffs across technical and non-technical audiences
Lead design and hands-on implementation of production-grade generative AI systems - agentic workflows, multi-step RAG pipelines, and LLM-powered applications integrated with enterprise data and services
Translate incoming GenAI requests from product, business, and platform stakeholders into well-scoped engineering work - applying technical judgment to surface tradeoffs, define scope, and set clear acceptance criteria
Define and implement reusable engineering patterns for prompt management, workflow versioning, structured outputs, tool orchestration, and rollback across production AI services
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
Continuously evaluate emerging AI models, tools, and architectural approaches, incorporating improvements into existing systems incrementally
Champion an iterative delivery culture - shipping incrementally, incorporating feedback, and improving continuously across all GenAI workstreams