AI Product Engineer

What matters most

• Turn use cases into deployed AI products

  The role works with Product Management to elicit GenAI requirements, evaluate approaches, and own delivery from solution design and model selection through deployment and improvement. Prepare to explain how you would translate a business use case into an evaluated, deployable solution.

• Build agentic workflows for business tasks

  The posting calls for agentic applications for risk analysis, information search, and audit and compliance checks, using planning, memory, reasoning, and tools. Prepare to discuss when an agentic workflow is warranted and how its steps would be controlled.

• Deliver cloud-native AI services

  The role develops Python services with REST or GraphQL APIs, background jobs, and event-driven systems, then deploys them with attention to scalability, latency, security, and cost. Prepare a production architecture walkthrough rather than focusing only on prompts.

• Operate and improve GenAI systems

  Responsibilities include CI/CD, prompt versioning, experiment tracking, and monitoring hallucinations and token usage, alongside guardrails, moderation, and data privacy. Prepare to explain what you would measure and how findings would drive changes.

• Work across teams and guide delivery

  The engineer works independently and with project teams, integrates AI services with other engineers, and contributes code reviews, documentation, knowledge sharing, and junior mentoring. Prepare examples of technical decisions communicated across teams.

Likely challenges to think through

• Balancing agent autonomy with control

  Preparation hypothesis: Multi-step tool use could help with complex risk or compliance tasks while making errors harder to trace. Be ready to describe tool boundaries, intermediate checks, and ways to inspect a workflow's results; this is not a confirmed employer issue.

• Reconciling quality, latency, and cost

  Preparation hypothesis: Model choice, retrieval, and additional agent steps may improve answers but add response time and token expense. Prepare to compare approaches against use-case requirements and explain what evidence would justify the trade-off.

• Keeping production behavior dependable

  Preparation hypothesis: Prompt or model changes may affect output quality after deployment. Prepare to discuss versioning, evaluation, monitoring for hallucinations and token usage, and how you would respond when observed behavior changes.

Topics to prepare

• GenAI use-case and model selection

  The role includes eliciting requirements with Product Management and choosing models and technologies. Exercise: outline an information-search use case, its success criteria, and a comparison of OpenAI, Claude, and Gemini on latency, cost, and suitability. Use the comparison as a preparation exercise, not as a claim about the company's selection process.

• Agentic system design

  The posting calls for reasoning, planning, memory, and tool use. Exercise: draw a workflow for an audit-check agent, marking each tool call, what state must persist, and when the agent should stop or hand off.

• RAG, prompting, and tokenization

  Prompt engineering, RAG architectures, and tokenization are explicit requirements. Exercise: sketch a retrieval-to-answer pipeline for information search, then compare two prompt designs for grounding answers and staying within a token budget.

• Production and responsible-AI trade-offs

  The role emphasizes secure, scalable, low-latency, cost-conscious deployment, guardrails, moderation, and data privacy. Exercise: review a hypothetical client-facing AI service design and identify one risk and mitigation for each of those concerns.

• LLMOps across the AI lifecycle

  The responsibilities span deployment, monitoring, and improvement, including prompt versioning, experiment tracking, hallucination checks, and token-usage monitoring. Exercise: define what to record for a prompt change and what evidence would justify retaining or reverting it.

Skills to revise

• Build a Python AI service

  Production-grade Python and REST AI services using frameworks such as FastAPI are required. Exercise: implement a small REST endpoint that accepts a search question, validates input, calls an LLM-backed workflow, and returns a structured response with error handling.

• Implement an agent workflow

  The posting names LangChain, LangGraph, LlamaIndex, and AutoGen as orchestration examples. Exercise: use one of them to build a two-step agent that invokes a tool, retains the needed state, and records its steps for inspection.

• Test a retrieval-backed answer

  RAG is an explicit requirement; vector databases are listed as preferred knowledge. Exercise: create a small document collection, retrieve passages for sample questions, and check whether generated answers are supported by those passages. Trying FAISS is optional preparation, not a stated requirement.

• Package and deploy a service

  The role asks for Docker, basic CI/CD knowledge, and cloud ML deployment knowledge; AWS is preferred, while Azure or GCP is acceptable. Exercise: containerize the Python service, add an automated test to a basic CI workflow, and rehearse how you would deploy and monitor it on one of those cloud platforms.

• Measure quality and operating cost

  Monitoring hallucinations and token usage is named in the responsibilities. Exercise: run a small set of known-answer queries through two prompt versions and log answer support, latency, and token usage to explain a release choice.

• Review AI-assisted code

  Strong use of tools such as Claude Code or Cursor is requested, alongside code reviews and documentation. Exercise: use one named tool to draft a small API change, then manually review its behavior, tests, privacy implications, and documentation before accepting it.

• Map the stated requirements to evidence

  Prepare a one-page list of examples you can discuss for production GenAI delivery, Python, agentic systems, REST APIs, cloud deployment and AI-assisted coding. Keep preferred skills, such as vector databases and LLMOps frameworks, separate from stated requirements.

• Prepare an end-to-end project walkthrough

  Choose an actual GenAI application you worked on and outline the requirements, approach and model selection, prompt or workflow changes, deployment, monitoring and improvements. Describe your own decisions and measurable results without disclosing confidential information.

• Sketch an agentic AI design

  As a preparation exercise, diagram a risk-analysis, information-search or audit-check workflow. Explain where reasoning, planning, memory and tools fit, and when RAG or a simpler workflow would be preferable.

• Walk through a Python service architecture

  Prepare a service diagram and API contract for exposing an AI workflow through REST, such as FastAPI. Be ready to explain background jobs or events, Docker deployment, and cloud choices; the posting prefers AWS but also accepts Azure or GCP.

• Define evaluation and responsible-AI checks

  Draft a small test set and evaluation plan covering answer quality, hallucinations, latency and token cost. Show where guardrails, moderation, security and data-privacy checks would enter the proposed workflow.

• Show how you would operate and improve it

  Prepare a concise release-and-monitoring outline with CI/CD, prompt versioning, experiment tracking and a response to a degraded result. These practices are listed in the role; label any example metrics or thresholds as your proposed choices.

• Prepare collaboration and coding examples

  Select examples of integrating an AI service with product, data or application engineers, contributing to reviews or documentation, and guiding a junior teammate. Be ready to demonstrate how you verify work produced with a tool such as Claude Code or Cursor.

• Bring targeted questions for the employer

  Ask how Product Management defines GenAI use cases, what production monitoring and evaluation look like, and how the AI team works with other engineers. The posting describes these responsibilities but does not specify the team's current implementation.