[Hiring] Senior 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

Reports to: Head of the AI Centre of Excellence

Technical direction from: Lead AI Architect — you will work alongside them daily, and you are expected to push back when a design will not survive contact with production.

Works with: The functional AI Engagement Specialists who own the workflows, PRDs and quality bar for the business functions in scope.

Also partners with QAD engineering teams, Data & Platform Engineering, Security & GRC, and external system integrators.

Scope: Foundational engineering hire. Expect to set the engineering standard, mentor subsequent hires, and review 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 — and default to the former wherever it produces the same outcome more reliably and more cheaply.
  • Integrate with enterprise systems — ERP, CRM, support, services delivery, knowledge and data platforms — including systems with imperfect APIs and imperfect data.
• 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 so that a user or an agent can only ever retrieve what that identity is entitled to see — non-negotiable in an enterprise context.
  • Handle freshness and incremental indexing so that retrieval reflects the state of the business rather than a snapshot from onboarding day.
  • Measure retrieval quality explicitly and treat it as a tunable subsystem with its own metrics, not as an assumption.
• Model selection, tuning and evaluation
  • Select, tune and route models across tiers based on measured quality, latency and cost — not on reputation or recency.
  • Own prompt engineering, versioning, structured output and context strategy as versioned, tested artefacts under source control.
  • Apply fine-tuning, adapters or distillation only where evidence shows the return justifies the operating burden — and be able to make that argument either way.
  • Build the evaluation harness: golden datasets, offline evaluation, LLM-as-judge with human calibration, regression suites running in CI, and online quality monitoring with structured feedback capture.
  • Work with the Engagement Specialists to turn a business quality bar into measurable criteria — and be honest when a workflow does not clear it.
• The reusable component library
  • Design, build and maintain the shared component library — SDKs, shared services, templates, reference implementations and documentation — that both internal engineers and partner pods build against.
  • Treat internal engineers and SI teams as customers of your library: versioning, backwards compatibility, examples, and documentation good enough that people use it without asking you.
  • Review partner-delivered code and designs for conformance, quality and maintainability.
• Production engineering and operations
  • Meet explicit latency and cost budgets per workflow, using caching, batching, streaming, model tiering and prompt efficiency.
  • Build for graceful degradation: provider outages, rate limits, backpressure, partial failures and safe fallbacks.
  • Implement observability for non-deterministic systems — full step-level tracing, token and cost telemetry, quality dashboards, and a triage path for incidents where nothing crashed but the output was wrong.
  • Own infrastructure as code, CI/CD, environment promotion, secret and credential handling, and testing discipline for everything the CoE ships.
  • Participate in the operational support model for live AI workflows, including post-incident review and remediation.
• Craft and team
  • Set the engineering standard for the CoE and raise it as the team grows.
  • Mentor subsequent engineering hires and partner engineers.
  • Document decisions and trade-offs so that the next engineer inherits reasoning, not just code.

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 — you have built a RAG or hybrid search system, measured it, found it wanting, and improved it.
• 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 (Bedrock, SageMaker, Vertex AI or equivalent).
• API design and enterprise integration, including authentication and authorisation patterns — OAuth 2.0 / OIDC.
• Containerisation, infrastructure as code (Terraform, CDK or equivalent), CI/CD and real testing discipline.
• Evaluation rigour — you can define what “good” means numerically for a subjective task.
• Cost and latency awareness as a design instinct, not an afterthought raised by finance.
• Clear written communication — design notes, documentation and honest status.
• Comfort with ambiguity and shifting priorities in an early-stage function.

Requirements

• Vector and hybrid search infrastructure at production scale — OpenSearch, pgvector, Vertex AI Search, Elasticsearch, Pinecone, Weaviate or similar.
• Evaluation and observability tooling — LangSmith, Langfuse, Phoenix, Braintrust, Ragas, DeepEval or equivalent.
• Multi-tenant SaaS engineering, and the data isolation discipline that comes with it.
• ERP, manufacturing or supply chain data experience.
• Classical ML and MLOps background — feature stores, model registries, monitoring and drift.
• Fine-tuning, parameter-efficient adaptation, or model serving and optimisation.
• Experience building internal platforms or SDKs consumed by other engineers.
• Open-source contribution in the AI engineering ecosystem.

Benefits

• Your health and well being are important to us at QAD. We provide 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 who support one another to get the job done.
• An atmosphere of growth and opportunity, where idea-sharing is always prioritized over level or hierarchy.
• Compensation packages based on experience and desired skill set.