[Hiring] Principal AI Engineer @QAD, Inc.

Role Description

The Senior AI Engineer is the primary builder in the CoE. You will take prioritised workflows from architecture and PRD through to working, evaluated, production-grade AI systems — and, just as importantly, factor what you build into a reusable component library so that the fifth workflow costs a fraction of the first.

This is an applied engineering role with unusually high leverage. You are not building one product; you are building the components, patterns and reference implementations that internal engineers and external partner pods will use to build many. The work spans:

• Retrieval
• Agent orchestration
• Evaluation
• Production engineering — latency, cost, failure handling, observability

Where this role sits:

• Reports to: Head of the AI Centre of Excellence
• Technical direction from: Lead AI Architect
• Works with: Functional AI Engagement Specialists
• Also partners with: QAD engineering teams, Data & Platform Engineering, Security & GRC, and external system integrators
• Scope: Foundational engineering hire, setting engineering standards, mentoring subsequent hires, reviewing partner-delivered code.

Key Responsibilities

• Building AI and agentic workflows:
  • Build production AI and agentic workflows end to end — from PRD and architecture through implementation, evaluation, release and iteration in production.
  • Design and implement agent orchestration: multi-step flows, tool and API invocation, planning and routing, state and memory, retries, fallbacks, timeouts and human-in-the-loop checkpoints.
  • Make the judgement call on deterministic control flow versus model-driven control flow.
  • Integrate with enterprise systems — ERP, CRM, support, services delivery, knowledge and data platforms.
• Retrieval and context engineering:
  • Own the retrieval stack: corpus onboarding, document parsing, chunking strategy, embedding selection, hybrid lexical and vector search, reranking, metadata filtering and query transformation.
  • Implement permission-aware retrieval.
  • Handle freshness and incremental indexing.
  • Measure retrieval quality explicitly.
• Model selection, tuning and evaluation:
  • Select, tune and route models based on measured quality, latency and cost.
  • Own prompt engineering, versioning, structured output and context strategy.
  • Build the evaluation harness: golden datasets, offline evaluation, LLM-as-judge with human calibration.
  • Work with Engagement Specialists to turn a business quality bar into measurable criteria.
• The reusable component library:
  • Design, build and maintain the shared component library.
  • Treat internal engineers and SI teams as customers of your library.
  • Review partner-delivered code and designs for conformance, quality and maintainability.
• Production engineering and operations:
  • Meet explicit latency and cost budgets per workflow.
  • Build for graceful degradation.
  • Implement observability for non-deterministic systems.
  • Own infrastructure as code, CI/CD, environment promotion, secret and credential handling.
  • Participate in the operational support model for live AI workflows.
• Craft and team:
  • Set the engineering standard for the CoE.
  • Mentor subsequent engineering hires and partner engineers.
  • Document decisions and trade-offs.

Qualifications

• 6+ years of professional software engineering, including 2+ years building and operating production LLM or GenAI systems.
• Excellent Python, plus working competence in at least one other language (TypeScript, Go, Java or similar).
• Genuine production retrieval experience.
• Agent orchestration experience with frameworks such as LangGraph, LlamaIndex, Semantic Kernel, Google ADK, Strands, CrewAI or equivalent.
• Strong AWS and/or GCP experience, including managed AI services.
• API design and enterprise integration experience.
• Containerisation, infrastructure as code, CI/CD and real testing discipline.
• Evaluation rigour.
• Cost and latency awareness as a design instinct.
• Clear written communication.
• Comfort with ambiguity and shifting priorities.

Requirements

• Vector and hybrid search infrastructure at production scale.
• Evaluation and observability tooling experience.
• Multi-tenant SaaS engineering experience.
• ERP, manufacturing or supply chain data experience.
• Classical ML and MLOps background.
• Fine-tuning, parameter-efficient adaptation, or model serving and optimisation experience.
• Experience building internal platforms or SDKs.
• Open-source contribution in the AI engineering ecosystem.

Benefits

• Programs that help you strike a healthy work-life balance.
• Opportunity to join a growing business, launching into its next phase of expansion and transformation.
• Collaborative culture of smart and hard-working people.
• An atmosphere of growth and opportunity.
• Compensation packages based on experience and desired skill set.