Senior Staff Engineer - AI Engineering

What matters most

• Align AI work with engineering needs

  The role defines and executes AI strategy for retrieval, code generation and AI-assisted development, working with product, engineering and business stakeholders. Prepare to explain how you would select use cases and judge whether they meet a development need.

• Build grounded retrieval and agent systems

  The posting calls for RAG pipelines, improved retrieval accuracy, and agents that interact with APIs, databases and development environments. Prepare to discuss how you would evaluate retrieval relevance, response grounding and agent behavior.

• Make code and test generation usable in workflows

  The role includes production-ready code generation, creation and validation of unit, integration and regression tests, and integration with IDE and CI/CD workflows. Prepare to explain validation steps rather than treating generated output as ready by default.

• Deploy and evaluate models at scale

  The posting specifies cloud-based MLOps, scalable model and retrieval deployments, low-latency inference, and design, training and evaluation of LLMs and standalone models. Prepare to discuss how model quality and runtime performance would be measured together.

• Set practices and mentor engineers

  The role guides 3–5 AI engineers and establishes practices for AI-assisted development, secure integration and bias mitigation. Prepare examples of technical guidance and how you would make those practices usable across a team.

Likely challenges to think through

• Retrieval quality versus response speed

  A likely trade-off is that more retrieval or checking may improve grounding while adding latency to the real-time systems described. Prepare to outline an evaluation approach that measures both answer quality and inference time; this is a preparation hypothesis, not a stated employer problem.

• Automation versus code assurance

  Generated code and tests may accelerate development without reliably catching defects. Prepare to explain how review, test validation and CI/CD checks could preserve quality while retaining automation benefits; the posting does not specify an existing failure.

• Agent capability versus secure integration

  Agents that access APIs, databases and development environments create a likely tension between useful autonomy and controlled access. Prepare to discuss permissions, action boundaries and evaluation of agent behavior as proposed safeguards, not confirmed internal requirements.

• Research breadth versus focused delivery

  The role combines emerging-model research, stakeholder alignment, deployment and mentorship. A likely challenge is choosing which approaches merit adoption without spreading effort too thinly. Prepare a way to compare a new approach with a simpler baseline against the stated engineering need.

Topics to prepare

• Retrieval quality and grounding

  Review how retrieval relevance affects context-aware answers; work through an example where retrieved material is incomplete or misleading and define how you would assess answer accuracy. The posting explicitly prioritizes RAG pipelines and grounded outputs.

• Agent architecture and secure integration

  Sketch an agent’s interactions with an API, a database and a development environment, then identify where access controls and validation belong. The role calls for autonomous agents and secure AI integration; the controls are a preparation exercise, not a specified implementation.

• Model adaptation and evaluation

  Compare prompt changes, fine-tuning and model evaluation for one domain-specific engineering task; define what evidence would justify each choice. The posting names prompt engineering, LLM fine-tuning, and training and evaluation of LLMs and standalone models.

• Code quality and test coverage

  Review a generated code change and map its failure cases to unit, integration and regression tests. This reflects the stated focus on production-ready code and AI-driven test creation and validation.

• Scalable inference trade-offs

  Explain how you would evaluate latency and performance as a model and retrieval pipeline scale, including what to measure before deployment. Cloud-based MLOps and low-latency real-time inference are explicit responsibilities.

• AI strategy and technical leadership

  Draft a short prioritization rationale for RAG versus code-generation work, including stakeholder needs, evaluation criteria and how you would guide a team of 3–5 engineers. The posting calls for strategy, cross-functional alignment and mentorship; this draft is a suggested preparation exercise.

Skills to revise

• Prototype and evaluate a RAG pipeline

  Build a small retrieval-and-answering example using sample engineering material, then inspect retrieval relevance and whether each answer is supported by retrieved content. This practices the posting’s RAG development and retrieval-accuracy responsibilities; the sample material is a suggested exercise, not an employer dataset.

• Connect an agent to development resources

  Prototype a bounded agent interaction with an API or database and document permitted actions, errors and safeguards. LangChain and OpenAI APIs are examples named in the posting; no particular framework is stated as mandatory.

• Validate generated code and tests

  Generate a modest code change and unit, integration and regression test cases; run the tests and review what they fail to catch. Then sketch where validation would sit in an IDE and CI/CD workflow, both explicitly mentioned in the role.

• Practice a model-training workflow

  Using Hugging Face Trainer or Fairseq, outline or run a small training-and-evaluation exercise and record how you would compare results with a prompt-only baseline. The posting asks for familiarity with these tools and domain-specific fine-tuning; the baseline is a suggested comparison.

• Rehearse deployment and latency measurement

  Sketch a Docker- and Kubernetes-based deployment of a model plus retrieval pipeline on one of the named cloud platforms, and specify latency and performance checks. This prepares for the stated cloud MLOps, scalable deployment and real-time inference work; the architecture is an exercise, not a prescribed company design.

• Map the stated qualifications to evidence

  Prepare a brief timeline of AI/ML work, highlighting any LLM, code-generation, RAG or automation experience, plus your relevant degree. The posting specifies 5+ years in AI/ML, at least 2 years in those areas, and a master's or Ph.D. in a related field.

• Prepare a RAG and agent project walkthrough

  Select a relevant project, if available, and diagram retrieval, grounding and evaluation choices. Be ready to explain how an agent interacted with APIs, databases or development tools, and what you measured to improve answer accuracy.

• Demonstrate code and test-generation decisions

  Prepare an example or proposed workflow showing how generated code and unit, integration and regression tests would be reviewed and validated before use. Include how an assistant could fit into IDE and CI/CD workflows.

• Practice a model-selection and deployment design

  Sketch a design for a domain-specific LLM application: compare prompting with fine-tuning, explain model evaluation and training-tool choices, and show a scalable retrieval and inference deployment. Address latency and performance trade-offs using the posting's cloud, Docker and Kubernetes context.

• Prepare a strategy and mentorship example

  Outline how you would align an AI-assisted engineering initiative with product, engineering and business needs. Bring an example of guiding engineers or, if more appropriate, a proposed approach to mentoring the advertised team of 3–5 and establishing secure-integration and bias-mitigation practices.

• Write targeted questions for the employer

  Ask which engineering knowledge sources and workflows are priorities for RAG and code assistance, how success and output quality would be measured, and what deployment and security constraints the team faces. These are questions to clarify, not assumptions about Lattice's current systems.