Senior AI Engineer

What matters most

• Build retrieval and context systems

  The role owns retrieval workflows, RAG, context management, and agent prompts behind security agents. Be ready to explain architectural choices for context windows, vector retrieval, and multi-step reasoning on customer data.

• Connect AI workflows to the product

  The posting calls for orchestrating workflows with LangChain, LlamaIndex, or similar tools and integrating them with product APIs and backend services. Prepare a production architecture example that shows how the AI components fit with APIs, databases, and cloud deployment.

• Improve quality through real-world evaluation

  The engineer owns prompt and template iteration and tracks usefulness, factual correctness, latency, and user-experience impact—not just accuracy. Prepare to describe how evaluation results informed a change to prompts or context strategy.

• Deliver safe, dependable agent outputs

  Structured output pipelines, hallucination mitigation, safety guardrails, and reliable user outcomes are explicit expectations. Be ready to discuss how tool calls and outputs are constrained or checked before they affect a security workflow.

• Operate efficiently in production

  The role calls for scalable deployments with attention to performance, cost efficiency, and observability, in partnership with product, platform, and backend teams. Prepare to explain operational trade-offs from an LLM application that shipped beyond a prototype.

Likely challenges to think through

• Keeping investigations grounded on real data

  Preparation hypothesis: The posting warns that retrieval and prompting can look convincing in a demo but fail on a customer's data. The trade-off is giving an agent enough relevant context without introducing distracting or unsupported evidence. Prepare an example of how you would diagnose retrieval failures and test factual correctness.

• Balancing reasoning depth with responsiveness

  Preparation hypothesis: Context windows, caching, tool calls, and multi-step reasoning can improve an investigation while increasing latency and cost. Prepare to explain where you would cache, limit steps, or simplify a workflow, and which quality and latency measures would guide that choice.

• Making agent outputs usable across services

  Preparation hypothesis: Structured outputs and backend integration must remain reliable even when model responses vary. The difficulty is preserving useful flexibility without passing malformed or unsafe results to downstream services. Prepare an example of output validation, failure handling, and how you would coordinate an integration boundary with backend teams.

Topics to prepare

• Retrieval and context design

  The posting calls for RAG, vector databases, retrieval workflows, context windows, and caching. Suggested exercise: diagram how a security agent would retrieve relevant investigation records, select what fits in context, and handle missing or conflicting evidence.

• Agent workflow architecture

  The role includes tool calls, multi-step reasoning, and experience with multimodal models or multi-agent design. Suggested exercise: sketch an investigation workflow with tool inputs, decision points, and failure paths; explain when a single agent would be simpler than multiple agents.

• Prompt reliability and output safety

  The posting emphasizes prompt iteration, hallucination mitigation, guardrails, and structured output enforcement. Suggested exercise: compare prompt and context strategies for an evidence-based finding, including what should happen when evidence is insufficient or an output fails validation.

• Product-facing AI evaluation

  The role tracks usefulness, factual correctness, latency, and user experience impact rather than accuracy alone. Suggested exercise: define a small evaluation set for security investigations and explain how you would compare two workflow versions across those measures.

• Production system trade-offs

  The posting prioritizes scalable deployments, performance, cost efficiency, observability, and clean integration with product and backend teams. Suggested exercise: walk through an end-to-end design and identify likely bottlenecks, cost drivers, observable failure signals, and integration boundaries.

Skills to revise

• Build a RAG retrieval path

  Hands-on RAG and vector database experience are explicit requirements; Pinecone and Weaviate are named examples. Suggested exercise: use sample investigation records to build a retrieval path, inspect irrelevant and missing results, and revise the context passed to the model.

• Orchestrate a tool-using workflow

  The posting names LangChain, LlamaIndex, Dust, or similar frameworks for AI workflows. Suggested exercise: implement a short retrieval-to-tool-call-to-answer flow in one named framework and trace how state and errors move between steps.

• Iterate prompts and enforce outputs

  Owning prompts, templates, context strategies, and structured outputs is part of the role. Suggested exercise: write two prompt versions for an investigation summary, require defined output fields, and test unsupported claims and malformed responses.

• Integrate a Python backend

  Strong Python or equivalent coding, API design, backend integration, and database systems are stated requirements. Suggested exercise: expose a small AI workflow through an API, persist its inputs and results, and handle invalid requests and downstream failures.

• Measure and diagnose quality

  The posting calls for real-world evaluation and production observability. Suggested exercise: run a fixed set of sample investigations through two versions, record factual correctness and latency alongside usefulness judgments, and explain a regression.

• Plan a production deployment

  Cloud deployment experience and reliable, scalable, cost-efficient operation are explicit in the posting. Suggested exercise: outline how you would deploy the API-backed workflow to a cloud environment, including caching, monitoring, failure handling, and cost checks; no particular cloud provider is specified.

• Map the stated requirements to evidence

  Prepare a one-page evidence map for the posting’s 6+ years of software engineering, at least one year of production AI, relevant degree, and shipped LLM applications. Use only experience and credentials you can substantiate.

• Build a production LLM project walkthrough

  If you have a relevant shipped project, prepare a concise account of your architecture, personal decisions, product contribution, deployment, measured outcomes, and lessons learned. The role emphasizes production systems over prototypes.

• Sketch a security-agent workflow

  As a preparation exercise, diagram how an agent might retrieve evidence for an investigation, manage context, call tools, and return structured output. Explain vector retrieval, caching, multi-step reasoning, and where a human stays on the loop; do not present the sketch as 7AI’s architecture.

• Prepare an evaluation and prompt-iteration example

  Show how you would compare prompt, template, and context changes using usefulness, factual correctness, latency, and user-experience measures named in the posting. Include an example failure and a proposed revision.

• Work through a reliability failure scenario

  Prepare a brief response to unsupported claims, unsafe tool actions, or malformed outputs in an agent workflow. Discuss guardrails, hallucination mitigation, structured-output enforcement, and how you would check that the fix works.

• Explain the path to production

  Prepare a diagram or talking points covering Python or equivalent code, API and backend integration, databases, cloud deployment, observability, scaling, and cost tradeoffs. Include how you would coordinate decisions with product, platform, and backend teams.

• Draft questions for the employer

  Ask how 7AI evaluates agent usefulness on customer data, divides responsibility between agents and humans, and balances latency, cost, and reliability. These are questions to clarify the role, not assumptions about its current processes.