AI Engineer Portal

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Interview Prep

Turn progress into interview readiness.

AI-powered coaching on 95 questions. Practice answers, get real-time feedback, and track your readiness score.

Readiness

Strengths

Gaps

Questions

This Week

1Monday: one timed Python or evaluation drill and a quick self-review on what still feels slow.

2Wednesday: one agent-system design answer plus one workflow orchestration note tied to a saved signal.

3Friday: refine one project story so you can explain scope, architecture, metrics, and tradeoffs clearly.

Focus Areas

Project proof that can survive interview scrutiny

Transition narrative and behavioral story structure

Agent control flow and tool orchestration

Skill Gaps

Interview communication

high

Interview readiness is currently 10/100.

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Python for AI Engineers

medium

Learning progress in this path is only 0.0%, so it is still underdeveloped.

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Next Moves

Move Evaluation Dashboard to complete status with tighter architecture notes and clearer outcomes.

Practice one system design and one behavioral answer each week using your actual project evidence.

Turn progress into interview readiness.

This Week

Focus Areas

Skill Gaps

Next Moves

Questions (95)

When does an agent architecture add value, and when is it just complexity?

How would you keep an agent workflow auditable?

When should a workflow stay deterministic instead of becoming agentic?

How would you design a tool schema for an LLM agent?

Explain the ReAct pattern and when to use it

How do you manage agent memory to stay within context limits?

What is MCP (Model Context Protocol) and why does it matter?

How would you evaluate an agent's performance in production?

Compare supervisor vs peer-to-peer multi-agent architectures

How would you design a backend boundary between product logic and provider-specific SDK calls?

How do you decide whether to persist intermediate AI artifacts?

How do you explain your transition from full-stack software engineering into AI engineering without sounding like you are starting over?

Tell me about a time you shipped an ambiguous product requirement.

How do you talk about an AI feature that failed its first production trial?

What changes when an AI feature moves from a demo to a real deployment?

What belongs in an AI service health check?

How would you deploy an LLM-powered feature that needs to handle 1000 requests per minute?

Explain your strategy for managing LLM API costs in a production application.

How do you implement graceful degradation when your primary LLM provider goes down?

Walk through how you'd set up CI/CD for an AI feature with prompt-based logic.

How do you implement token-aware rate limiting for a multi-tenant LLM API?

Walk through how you would add distributed tracing to an LLM pipeline to diagnose latency and quality issues.

How do you safely roll out a new prompt version to production without risking a quality regression?

How would you design cost monitoring and alerting for a production LLM service used by thousands of users?

Describe the observability stack you would build for a production RAG service, covering both system metrics and AI-specific signals.

How do you implement semantic caching for an LLM service, and what are the tradeoffs in choosing the similarity threshold?

What is the difference between a liveness probe and a readiness probe, and why does it matter for LLM services?

How would you design an observability system for a production LLM application?

How do you implement and tune a semantic cache for an LLM-powered feature?

Walk through how you'd safely roll out a new model version to production with minimal user impact.

How do you prevent LLM API costs from spiraling in a production application?

What does a production-ready LLM service health check endpoint look like?

What metrics would you put on an AI observability dashboard for a production feature?

What makes an evaluation metric useful instead of decorative?

How would you debug disagreement between an automated judge and a human reviewer?

How would you set up an evaluation pipeline for a new LLM feature before it goes to production?

What observability signals would you instrument for an AI-powered search feature?

How do you detect when an LLM feature's quality has degraded in production?

Compare LLM-as-judge evaluation with deterministic metrics. When would you use each?

Walk me through how you would set up an LLM-as-judge evaluation pipeline for a production RAG feature.

How would you design a regression testing system that catches quality degradation in an LLM feature before it reaches production?

How do you measure and control the cost of running LLM evaluations in production without sacrificing quality signal?

Explain how you would set up structured tracing for an LLM application and what you would include in each trace.

How do you detect and respond to quality drift in an LLM feature after deployment?

How would you design an A/B evaluation framework for comparing two prompt variants before shipping?

How would you design a golden dataset for a new LLM feature? Walk through the process from scratch.

How do you design and maintain a human feedback loop for an LLM feature in production?

How do prompt, retrieval, tools, and memory interact in an LLM application?

How would you design a prompt management system for a production app with multiple LLM features?

Explain the tradeoffs between JSON mode, function calling, and free-text parsing for structured LLM output

How do you handle prompt injection in a production application?

Walk through how you'd debug a production LLM feature that's returning poor quality responses

How would you design a provider-agnostic LLM client with fallback support for a production application?

Walk me through how you manage token budgets and conversation history in a multi-turn LLM feature.

How do you design a cost-tracking and cost-control system for a multi-feature LLM application?

How would you implement and operate a multi-turn conversation system that handles 10,000 concurrent sessions?

Explain the difference between prompt injection and jailbreaking, and how you defend against each in a production LLM application.

How would you build a prompt optimization workflow that systematically improves LLM output quality without manual trial and error?

How would you structure a Python service that wraps an LLM provider and remains testable as providers change?

What Python patterns matter most when moving from web product work into AI engineering?

How would you structure evaluation scripts so they are rerunnable and trustworthy?

How do you explain the role of Pydantic in an AI backend?

How would you design a Python service that wraps multiple LLM providers while remaining testable?

Explain how you'd use async/await to make concurrent LLM API calls with rate limiting

What Pydantic patterns do you use when validating LLM outputs that might be malformed?

How do you handle memory efficiently when processing large document collections for RAG ingestion?

How do you use Python generators to build memory-efficient data pipelines for large AI corpora?

How do you handle unreliable LLM structured output in production — when the model sometimes returns malformed JSON or violates the schema?

How do you track token usage and compute costs across multiple LLM calls in a multi-feature AI backend?

How do you design a Python retry policy that handles rate limits, transient server errors, and authentication failures differently?

What is the difference between using TypedDict, dataclasses, and Pydantic BaseModel in Python AI backends, and when would you choose each?

How would you implement rate-limited batch processing for a large document corpus that needs to stay within provider RPM and TPM limits?

How do you handle LLM structured output failures in production? Walk me through your validation and recovery strategy.

Explain how you implement retry logic for provider API calls. What is different about retrying AI provider calls versus retrying a standard REST API?