AI Engineer

What matters most

• Deliver AI applications from prototype to production

  The role develops LLM-based tools, copilots and automation, plus the APIs, services and data pipelines that support them. It calls for turning defined requirements into scalable, maintainable software that follows team architecture.

• Operate and improve deployed systems

  Production responsibilities include monitoring, evaluation, logging, CI/CD and application or model lifecycle management, as well as debugging performance and reliability.

• Apply security and quality controls

  The posting calls for security, privacy and responsible-AI practices, along with validation, testing and documentation under company processes, including QMS where applicable.

• Coordinate delivery across teams

  The engineer prototypes use cases with business, engineering and lab-services stakeholders, participates in design discussions, sprint planning and code reviews, and communicates risks and trade-offs to the manager and stakeholders.

Likely challenges to think through

• Balancing prototype speed with maintainability

  A promising use case may need a quick demonstration, while production work must fit established architecture and standards. As preparation, outline how to assess a use case, test a prototype and decide what must change before deployment; this is a likely trade-off, not a stated company problem.

• Keeping LLM behavior measurable in production

  LLM outputs can vary, making evaluation and monitoring harder than a simple pass-or-fail test. Prepare an approach to checking output quality, logging failures and investigating reliability or performance without assuming a particular model or metric is used at Atlas.

• Reconciling AI utility with controls

  Useful data access and tool calling may conflict with privacy, access control, auditability or validation needs. Prepare to explain how you would define boundaries, evaluate generated outputs and use risk-based tests; the posting does not specify a particular compliance regime.

Topics to prepare

• LLM application design

  Posting: Experience with prompting, tool/function calling, RAG, embeddings, vector databases, guardrails and evaluation is required. Suggested revision exercise: diagram a copilot that retrieves documents and calls a function; explain where retrieval errors, unsafe outputs or failed calls could occur.

• Production AI lifecycle

  Posting: The role spans prototyping, data pipelines, deployment, evaluation, monitoring, maintenance and MLOps. Suggested revision exercise: trace one hypothetical AI application from prototype to production and identify what must change at each stage.

• Modular architecture and cloud integration

  Posting: The role builds APIs and services, uses Azure or AWS, and follows team architecture and engineering standards. Suggested revision exercise: sketch component boundaries for an AI service and explain how the design supports scaling and maintenance.

• Security, privacy and responsible AI

  Posting: Required knowledge includes access control, governance and auditability, alongside secure deployment. Suggested revision exercise: review a hypothetical request containing sensitive data and identify access, logging and AI-output risks.

• Verification and quality practices

  Posting: The role supports validation, documentation and risk-based testing; QMS applies where applicable. Suggested revision exercise: rank failure modes for an AI feature by risk and explain what evidence would validate its behavior.

• Use-case selection and technical trade-offs

  Posting: The engineer helps identify high-value use cases, critically evaluates AI-generated outputs and communicates risks to stakeholders. Suggested revision exercise: compare an AI-assisted approach with a non-AI approach for one hypothetical lab-services task, stating assumptions and limitations.

Skills to revise

• Prototype and test a RAG workflow

  Posting: Hands-on LLM/RAG application experience is required. Suggested exercise: build a small retrieval-and-answer prototype with embeddings and a vector database, then test irrelevant retrieval, unsupported answers and a guardrail response.

• Implement an AI-facing API and data pipeline

  Posting: The role develops and integrates APIs, services and supporting data pipelines. Suggested exercise: create a small service that accepts input, processes records for an AI feature and returns a structured response; test malformed and missing inputs.

• Instrument and evaluate an application

  Posting: Responsibilities include monitoring, evaluation, logging, debugging and reliability. Suggested exercise: define a small evaluation set, record outcomes and failures, and use the results to diagnose one performance or reliability issue.

• Practice deployment and lifecycle changes

  Posting: Azure or AWS, CI/CD and secure deployment are required areas of experience. Suggested exercise: plan a deployment on either named cloud platform, including a CI/CD check, access controls, a monitored release and a rollback path.

• Write risk-based tests and review notes

  Posting: The role supports validation, quality documentation and code reviews. Suggested exercise: write tests for a high-risk AI failure, document expected versus observed behavior, and draft a concise review comment explaining the remaining risk.

• Map your evidence to the requirements

  Prepare a one-page list of examples or records you can discuss for the required degree, 2+ years of relevant work, production AI/ML delivery, Azure or AWS, APIs, and CI/CD. Keep preferred certifications and embedded ML separate from requirements.

• Rehearse a production project walkthrough

  If you have a relevant project, prepare a concise account of the requirements, prototype, architecture, data pipeline, deployment, and your contribution. Explain what changed between prototype and production and why.

• Practice an AI application design exercise

  Sketch a hypothetical LLM tool for an Atlas business or lab-services stakeholder; this is practice, not a stated company project. Show an API and data flow, where RAG or tool calling would help, and how the design would fit established architecture.

• Prepare an evaluation and validation plan

  Draft test cases and success measures for the hypothetical tool, including output quality, guardrails, failure modes, and risk-based testing. Identify what would need human review and what documentation you would retain.

• Explain how you would operate it in production

  Create a brief deployment and maintenance outline covering CI/CD, logging, monitoring, debugging, performance, and reliability. Include access control, data privacy, and auditability in the design.

• Prepare a collaboration example

  Choose an example you can accurately describe of handling competing tasks or a technical trade-off. Practice explaining the decision, risks, and progress in terms suitable for a software engineering manager and cross-functional stakeholders.

• Bring targeted questions for the team

  Write questions about current AI use cases, architecture standards, production evaluation practices, and when quality-management processes apply. Ask how engineering, product, and lab-services stakeholders prioritize prototypes for deployment.