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Applied AI Engineer

Remote · Global · Full-time

Build governed AI capabilities into enterprise platforms — retrieval, evaluation, and production systems that institutions can trust. You ship intelligence with measurement, not demos without accountability.

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The role

As Applied AI Engineer at AdvanzoTech, you design and deliver AI features that sit inside real operational workflows. You treat evaluation, safety boundaries, and observability as equal to model selection — so intelligence improves outcomes without creating ungoverned risk.

You collaborate with product, platform, and client stakeholders to compose retrieval pipelines, agentic patterns where justified, and feedback loops that keep quality honest. Your bar is production fitness: clear failure modes, audit-ready behaviour, and systems operators can understand and escalate.

Responsibilities

  • Design and implement applied AI features for enterprise products and client platforms.
  • Build evaluation harnesses, regression suites, and monitoring for model and prompt behaviour in production.
  • Compose retrieval, grounding, and tool-use patterns with clear governance and data boundaries.
  • Partner with engineering and product to integrate AI into existing architectures without brittle shortcuts.
  • Document assumptions, risks, and operational runbooks for intelligent systems in institutional settings.

Requirements

  • Hands-on experience shipping LLM or ML features to production with measurable quality controls.
  • Strong software engineering foundations (APIs, data pipelines, testing, observability).
  • Practical fluency with evaluation methods, prompt/system design, and retrieval architectures.
  • Ability to communicate trade-offs clearly to technical and non-technical stakeholders.
  • Discipline around privacy, data lineage, and safe failure behaviour in enterprise contexts.

Nice to have

  • Experience in regulated industries or high-assurance AI deployments.
  • Familiarity with vector stores, RAG evaluation frameworks, and offline/online experiment design.
  • Background in classical ML or information retrieval alongside modern LLM systems.

Hiring process

A process with institutional manners.

We evaluate craft and judgment — not puzzle theatre. You will always know where you stand.

  1. 01

    Application

    Share your background, a brief letter, and optional portfolio. We read carefully.

  2. 02

    Conversation

    A focused discussion with hiring partners about craft, constraints you have navigated, and fit.

  3. 03

    Craft exercise

    A realistic work sample or architecture walkthrough — never an arbitrary algorithm gauntlet.

  4. 04

    Team panel

    Meet peers across disciplines. Mutual evaluation of standards and collaboration style.

  5. 05

    Offer & onboarding

    Clear terms, thoughtful ramp, and a named buddy so you ship with confidence early.

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We review every submission. Expect a thoughtful reply — not an automated void.

Your details are used only for hiring evaluation. Future ATS sync will inherit the same privacy posture.

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