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04 - STAR Story System for AI Engineer Interviews

This module helps you convert technical depth into clear interview stories.

Use STAR+T

  • Situation: Context and constraints
  • Task: Your ownership and success criteria
  • Action: What you designed, built, debugged, and measured
  • Result: Quantified impact
  • Tradeoffs: Why this approach, what you rejected

Build an 8-Story Bank

Use one story for each capability:

  1. Built RAG application
  2. Debugged poor AI quality
  3. Built tool-calling/agentic workflow
  4. Improved latency and cost
  5. Created evaluation and monitoring loop
  6. Deployed to production
  7. Led AI coding workflow with guardrails
  8. Created standards/playbook

At minimum, keep the baseline six ready at all times:

  1. Built an LLM or RAG system
  2. Debugged a production quality issue
  3. Improved latency or cost
  4. Designed evaluation framework
  5. Deployed and operated in production
  6. Used AI coding tools to accelerate delivery safely

Story Mapping Matrix

Story Signals You Demonstrate
RAG build architecture, retrieval quality, grounding
Debugging trace literacy, diagnosis, reliability mindset
Agent workflow orchestration, safety, control boundaries
Cost/latency pragmatic optimization and model routing
Evals/monitoring measurable quality ownership
Production deployment operational maturity
AI coding pilot modern developer workflow leadership
Standards/playbook scaling practices across teams

Week 4 Alignment

Day Use this page for Deliverable
Day 22 Role targeting and evidence mapping Competency matrix
Day 23 STAR story drafting and compression Six story bank
Day 24 Portfolio narrative and case-study framing Case-study outline

Step-by-Step Story Build Sequence

Step Action Output
1 Pick one project with real constraints Story candidate
2 Write facts before writing polish Raw notes
3 Name your ownership clearly Strong Task section
4 Add metrics, latency, cost, or quality impact Quantified Result
5 Explain one decision you rejected Tradeoff line
6 Compress to 90 seconds, then expand to 3 minutes Interview-ready pair

Example Template: Story Card

## Story: Improved RAG Answer Quality

Situation:
The support assistant was returning incomplete answers for account-security questions.

Task:
I owned retrieval quality and needed to improve grounded answer accuracy before launch.

Action:
I logged retrieved chunks, reworked chunking boundaries, added hybrid retrieval, and introduced
citation-only answer rules.

Result:
Retrieval hit rate improved from 0.61 to 0.84, faithfulness increased by 11 points, and support
review time dropped by 22 percent.

Tradeoff:
Hybrid retrieval added some latency, so I limited reranking to high-value queries.

Example Table: Role-to-Evidence Matrix

Role signal Evidence to attach
LLM architecture diagram, API flow, prompt builder
RAG quality eval table, failure log, citations
Agent reliability workflow state diagram, approval gate
Production ops release gate, trace record, postmortem

90-Second Formula

Project and context
-> your ownership
-> architecture and implementation
-> core challenge and tradeoff
-> measurable result
-> key lesson

STAR+T Scoring Rubric

Before using a story in interviews, check:

  • [ ] Includes concrete scope and constraints
  • [ ] Names your specific ownership
  • [ ] Includes 1 to 3 measurable outcomes
  • [ ] Explains one design tradeoff clearly
  • [ ] Ends with learning and next improvement

3-Minute Deep Dive Formula

Context and constraints
-> architecture details
-> failure modes
-> evaluation and metrics
-> deployment and reliability
-> what you would improve next

Quick Lab (15 min)

STAR compression lab
  • Pick one project.
  • Write STAR+T in 8 bullet points.
  • Convert it to a 90-second answer.
  • Record yourself once and tighten language.
Interview Q: What makes a STAR story credible for an AI engineer role?

Model Answer: A credible story includes technical constraints, your explicit ownership, measurable outcomes, and one clear tradeoff. Without those, it sounds like summary language instead of evidence from real engineering work.

Why this matters: Interviewers use stories to test both substance and judgment.

Interview Q: How do you avoid sounding vague when describing AI projects?

Model Answer: I anchor the story in artifacts and metrics such as retrieval hit rate, latency, evaluation gates, or deployment outcomes. That keeps the answer concrete and makes the impact testable.

Why this matters: Clear evidence separates practiced answers from real execution.

Next: 05 Interview Sprint Loops

STAR Stories Preparation Guide for AI Engineer / Agentic AI / LLMOps Roles

Purpose: Use this as a practical interview reference for roles involving LLM applications, RAG, agents, LangGraph/LangChain, MCP, AI coding tools, production deployment, evaluations, observability, and guardrails.

1. Why STAR Stories Matter for These Roles

For these jobs, interviewers are looking for proof that you can:

  • Build production-grade LLM applications, not just demos.
  • Own systems end-to-end: architecture, coding, debugging, deployment, monitoring, and iteration.
  • Handle ambiguity, failures, latency, cost, and quality issues.
  • Communicate tradeoffs clearly to engineering, product, and leadership.
  • Use AI tools responsibly in enterprise or startup environments.

A strong STAR story should prove one or more of these signals:

Ownership + Technical Depth + Production Reliability + Measurable Impact

2. STAR Format for AI/LLM Roles

Classic STAR means:

  • S — Situation: What was the business/product/technical context?
  • T — Task: What were you responsible for?
  • A — Action: What exactly did you design, build, debug, or improve?
  • R — Result: What changed? Use metrics where possible.

For AI engineering roles, extend STAR into STAR+T:

  • T — Tradeoffs: What alternatives did you consider and why did you choose your approach?

STAR+T Structure

Situation:
  What was the context? What problem existed?

Task:
  What was your responsibility? What did success look like?

Action:
  What architecture, tools, code, workflows, evals, or processes did you implement?

Result:
  What measurable impact happened? Reliability, cost, latency, quality, adoption, revenue, retention?

Tradeoffs:
  What options did you reject? What constraints shaped your decision?

3. STAR Story Selection Matrix

Prepare at least 8 reusable stories. Each story should map to multiple interview questions.

Story Type What It Proves Typical Interview Question
Built an LLM/RAG app Hands-on GenAI development Tell me about an AI system you built.
Debugged bad AI output Reliability mindset How do you debug hallucinations or bad retrieval?
Built an agent/tool workflow Agentic AI skill How have you used tool calling or agents?
Improved latency/cost Production maturity How do you optimize LLM systems?
Created evals/monitoring LLMOps maturity How do you measure quality?
Deployed to production Engineering depth How do you ship AI systems?
Used AI coding tools/MCP Modern AI engineering How do you use Cursor/Copilot/Claude Code safely?
Led standards/playbooks Leadership/governance How do you enable teams with AI best practices?

4. Mermaid Diagram: How STAR Stories Map to Interview Signals

flowchart TD A[STAR Story] --> B[Situation] A --> C[Task] A --> D[Action] A --> E[Result] A --> F[Tradeoffs] D --> G[Architecture] D --> H[Code / Implementation] D --> I[Evals / Testing] D --> J[Deployment] D --> K[Observability] E --> L[Business Impact] E --> M[Technical Impact] E --> N[User Impact] F --> O[Why this approach?] F --> P[What did you reject?] F --> Q[What would you improve?]

5. The 8 Most Useful STAR Stories to Prepare

Story 1: Built a RAG-Based Knowledge Assistant

Use this for questions like:

  • Tell me about an LLM application you built.
  • How do you implement RAG?
  • How do you reduce hallucination?
  • How do you evaluate retrieval quality?

STAR Template

Situation:
  The team needed a way to answer questions from internal documents / policies / product docs / support tickets.
  Existing search was slow, keyword-based, and users often received incomplete answers.

Task:
  I was responsible for designing and implementing a RAG-based assistant that could retrieve relevant context and generate grounded answers with citations.

Action:
  I built a document ingestion pipeline, chunked documents by semantic sections, generated embeddings, stored them in a vector database, and created a retrieval flow.
  I added metadata filtering, top-k retrieval, prompt grounding, structured output, citations, and fallback behavior when confidence was low.
  I created a small eval set of representative questions to test retrieval hit rate, answer faithfulness, and hallucination cases.

Result:
  The assistant improved answer quality, reduced manual lookup time, and created a repeatable pattern for future knowledge-assistant use cases.
  If metrics are available, mention them: retrieval accuracy, latency, reduction in support tickets, user adoption, or time saved.

Tradeoffs:
  I chose RAG over fine-tuning because the knowledge changed frequently and needed source-grounded answers.
  I used chunking plus metadata filtering instead of only raw vector search because enterprise documents often require access control and domain-specific filtering.

Architecture Diagram

flowchart LR A[Documents] --> B[Parsing and Cleaning] B --> C[Chunking] C --> D[Embeddings] D --> E[Vector DB] F[User Query] --> G[Query Embedding] G --> E E --> H[Retriever] H --> I[Reranker / Metadata Filter] I --> J[Prompt Builder] J --> K[LLM] K --> L[Grounded Answer + Citations] L --> M[Logs / Evals]

Code Talking Point

# Simplified RAG flow pseudo-code
query = "What is the refund policy for enterprise customers?"

query_embedding = embedding_model.embed(query)
retrieved_chunks = vector_db.search(
    embedding=query_embedding,
    top_k=5,
    filters={"department": "policy", "region": "US"}
)

prompt = build_grounded_prompt(query, retrieved_chunks)
response = llm.generate(prompt)

validated = validate_answer_has_citations(response)
log_trace(query, retrieved_chunks, response, validated)

Story 2: Debugged a Poor-Quality RAG or LLM System

Use this for questions like:

  • Your RAG system gives wrong answers. What do you do?
  • How do you debug hallucinations?
  • How do you improve AI quality?

STAR Template

Situation:
  Users reported that the AI assistant gave incomplete or incorrect answers for certain questions.

Task:
  I needed to identify whether the issue was caused by retrieval, prompting, model behavior, or missing source data.

Action:
  I inspected traces and separated the pipeline into stages: document availability, chunking, retrieval, reranking, prompt construction, LLM output, and validation.
  I discovered that relevant information was split across chunks or not retrieved due to weak metadata and poor chunk boundaries.
  I adjusted chunking, added metadata filters, improved query rewriting, added reranking, and updated prompts to require citations and refusal when context was insufficient.
  I created regression tests so the same failures would not reappear.

Result:
  Retrieval quality improved, hallucinations reduced, and the system became easier to debug because each stage had logs and eval checks.

Tradeoffs:
  I avoided solving the issue only through prompt changes because the root cause was retrieval quality.
  I preferred pipeline-level fixes over manual prompt patching.

Debugging Flow

flowchart TD A[Bad Answer Reported] --> B{Does source data contain answer?} B -- No --> C[Fix ingestion / source coverage] B -- Yes --> D{Was relevant chunk retrieved?} D -- No --> E[Fix chunking / embeddings / filters / hybrid search] D -- Yes --> F{Was context used correctly?} F -- No --> G[Fix prompt / context ordering / instructions] F -- Yes --> H{Output valid and grounded?} H -- No --> I[Add validation / citations / refusal rules] H -- Yes --> J[Add eval regression case]

Story 3: Built an Agentic Workflow with Tool Calling

Use this for questions like:

  • Tell me about your experience with agents.
  • How do you design tool-calling workflows?
  • How do you make agents reliable?

STAR Template

Situation:
  A business process required multiple steps across systems, such as classifying a request, retrieving context, calling an API, verifying the action, and notifying the user.

Task:
  I was responsible for designing an AI-assisted workflow that could reason over the request but execute actions safely and reliably.

Action:
  I designed a hybrid workflow where deterministic code handled state transitions, permissions, retries, and validation, while the LLM handled classification, summarization, and reasoning.
  I defined tool schemas, added input/output validation, implemented retry limits, and logged every tool call.
  For high-risk actions, I added human approval or dry-run mode.

Result:
  The system automated a multi-step workflow while maintaining control, auditability, and reliability.

Tradeoffs:
  I avoided a fully autonomous agent because it was harder to test and control.
  I used a constrained state-machine approach to make behavior more predictable.

Agent Workflow Diagram

stateDiagram-v2 [*] --> ReceiveRequest ReceiveRequest --> ClassifyIntent ClassifyIntent --> RetrieveContext RetrieveContext --> PlanAction PlanAction --> ValidatePlan ValidatePlan --> HumanApproval: high risk ValidatePlan --> ExecuteTool: low risk HumanApproval --> ExecuteTool: approved HumanApproval --> Cancelled: rejected ExecuteTool --> VerifyResult VerifyResult --> Retry: failed and retryable Retry --> ExecuteTool VerifyResult --> FinalResponse: success VerifyResult --> Escalate: failed after retries FinalResponse --> [*] Escalate --> [*] Cancelled --> [*]

Tool Schema Example

from pydantic import BaseModel, Field
from typing import Literal


class CreateTicketInput(BaseModel):
  user_id: str
  issue_summary: str
  priority: Literal["low", "medium", "high"]
  category: Literal["billing", "technical", "account", "other"]


def create_ticket_tool(payload: CreateTicketInput):
  """Create a support ticket after validating user request."""
  # Validate permissions
  # Call ticketing API
  # Return ticket ID and status
  return {"ticket_id": "TCK-12345", "status": "created"}

Story 4: Improved Latency and Cost of an LLM System

Use this for questions like:

  • How do you optimize LLM cost?
  • How do you reduce latency?
  • How do you handle model routing?

STAR Template

Situation:
  An LLM workflow was useful but too slow or expensive for production usage.

Task:
  I needed to reduce cost and latency without significantly degrading quality.

Action:
  I analyzed traces and token usage to identify expensive steps.
  I introduced model routing: small/cheap models for classification and extraction, stronger models for complex reasoning.
  I reduced prompt size, compressed context, cached repeated results, streamed responses, and added timeouts/fallbacks.
  I tracked cost per successful task, not just cost per request.

Result:
  The workflow became faster and more cost-effective while preserving quality for high-value tasks.

Tradeoffs:
  I did not use the strongest model for every step because many tasks did not require deep reasoning.
  I balanced quality, cost, and latency based on task criticality.

Model Routing Diagram

flowchart TD A[User Request] --> B[Intent Classifier] B --> C{Task Complexity} C -- Simple extraction/classification --> D[Small Fast Model] C -- RAG answer --> E[Mid-tier Model] C -- Complex reasoning --> F[Strong Reasoning Model] D --> G[Validation] E --> G F --> G G --> H{Valid?} H -- Yes --> I[Return Response] H -- No --> J[Fallback / Retry / Escalate]

Story 5: Created Evaluation and Monitoring for LLM Apps

Use this for questions like:

  • How do you evaluate LLM applications?
  • How do you know a prompt change improved the system?
  • How do you monitor agent quality?

STAR Template

Situation:
  The AI system was being updated frequently, but quality was hard to measure consistently.

Task:
  I needed to create a repeatable evaluation and monitoring process.

Action:
  I created a golden dataset with representative queries, expected sources, expected behavior, and failure cases.
  I added evaluation checks for retrieval hit rate, faithfulness, answer relevance, structured-output validity, tool-call accuracy, latency, and cost.
  I logged traces for prompt, model, retrieved context, tool calls, outputs, and user feedback.
  I used regression tests before deploying prompt/model/retriever changes.

Result:
  The team could compare changes objectively and catch regressions before production.

Tradeoffs:
  I combined automated evals with human review because LLM quality is partly subjective and task-specific.

Evaluation Pipeline

flowchart LR A[Golden Dataset] --> B[Run Pipeline] B --> C[Collect Outputs] C --> D[Automated Metrics] C --> E[LLM-as-Judge] C --> F[Human Review] D --> G[Quality Report] E --> G F --> G G --> H{Deploy?} H -- Pass --> I[Release] H -- Fail --> J[Fix Prompt/Retriever/Tools]

Eval Config Example

rag_eval:
  dataset: golden_questions_v1.jsonl
  metrics:
    - retrieval_hit_rate
    - context_precision
    - faithfulness
    - answer_relevance
    - citation_accuracy
  thresholds:
    retrieval_hit_rate: 0.85
    faithfulness: 0.90
    citation_accuracy: 0.90

agent_eval:
  metrics:
    - task_success_rate
    - tool_call_accuracy
    - retry_rate
    - average_steps
    - cost_per_successful_task

Story 6: Deployed an AI System to Production

Use this for questions like:

  • How do you deploy LLM apps?
  • What production concerns do you consider?
  • How do you handle secrets, monitoring, and failures?

STAR Template

Situation:
  A prototype LLM application needed to be turned into a reliable production service.

Task:
  I was responsible for productionizing the system and making it maintainable.

Action:
  I wrapped the AI workflow in a FastAPI service, containerized it with Docker, configured secrets through a secret manager, added structured logs, health checks, retries, timeouts, and error handling.
  I separated configuration from code, created CI/CD checks, added evaluation gates, and documented deployment and rollback steps.

Result:
  The system moved from demo stage to a deployable service with monitoring, versioning, and operational controls.

Tradeoffs:
  I avoided putting too much orchestration logic inside prompts and kept critical business rules in deterministic code.

Production Deployment Diagram

flowchart TD A[Client / UI] --> B[API Gateway] B --> C[FastAPI AI Service] C --> D[LLM Provider] C --> E[Vector DB] C --> F[External APIs / Tools] C --> G[Database] C --> H[Tracing / Logs] C --> I[Metrics / Alerts] J[CI/CD] --> C K[Secret Manager] --> C

FastAPI Skeleton

from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

app = FastAPI(title="AI Workflow Service")


class QueryRequest(BaseModel):
  user_id: str
  question: str


@app.post("/ask")
async def ask(request: QueryRequest):
  try:
    result = await run_ai_workflow(
        user_id=request.user_id,
        question=request.question
    )
    return result
  except TimeoutError:
    raise HTTPException(status_code=504, detail="AI workflow timed out")
  except Exception as e:
    # Log exception with trace ID
    raise HTTPException(status_code=500, detail="Internal error")

Story 7: Used AI Coding Tools Safely in Engineering Workflows

Use this for questions like:

  • How do you use Cursor, Claude Code, or Copilot?
  • How would you lead an AI coding pilot?
  • What guardrails are needed for AI-assisted development?
  • What is MCP used for?

STAR Template

Situation:
  The engineering team wanted to use AI coding tools to improve productivity, but there were concerns around security, quality, and consistency.

Task:
  I was responsible for designing a pilot workflow and defining standards for safe usage.

Action:
  I identified high-value use cases: test generation, refactoring, documentation, code explanation, and migration assistance.
  I configured tool access, repo context, and approved usage patterns.
  I defined guardrails: no secrets in prompts, mandatory code review, tests required for AI-generated changes, license/security checks, and auditability.
  I created quick-start guides, prompt templates, and examples for developers.

Result:
  The pilot helped developers use AI tools more consistently while reducing risk.

Tradeoffs:
  I positioned AI coding tools as accelerators, not replacements for engineering judgment.
  I required human review for production changes.

AI Coding Pilot Diagram

flowchart TD A[Developer] --> B[Cursor / Claude Code / Copilot] B --> C[MCP Servers] C --> D[Repo Search] C --> E[Docs Search] C --> F[Test Runner] C --> G[Issue Tracker] C --> H[Security Scanner] B --> I[Generated Code / Refactor / Tests] I --> J[Human Review] J --> K[CI Tests] K --> L[Merge] M[Guardrails] --> B M --> J M --> K

AI Coding Guardrails Checklist

- [ ] Do not paste secrets, credentials, customer data, or sensitive code into unapproved tools.
- [ ] Use approved enterprise accounts and configured IDE extensions only.
- [ ] Require human review for all AI-generated code.
- [ ] Require tests for generated or refactored logic.
- [ ] Run static analysis and security scans.
- [ ] Track productivity and quality metrics.
- [ ] Document successful prompts and workflows.
- [ ] Use MCP/context tools with least-privilege access.

Story 8: Created Engineering Standards, Guardrails, or Playbooks

Use this for questions like:

  • How do you enable teams with AI best practices?
  • How do you create GenAI governance?
  • How do you mentor engineers in AI adoption?

STAR Template

Situation:
  Multiple teams were experimenting with LLMs, but practices were inconsistent and risk controls were unclear.

Task:
  I needed to create reusable standards and playbooks that helped teams build safely and faster.

Action:
  I created templates for prompt design, RAG architecture, tool calling, evaluation, observability, deployment, and security review.
  I defined approval criteria for production readiness and created examples for common workflows.
  I held enablement sessions and gathered feedback from developers using the playbooks.

Result:
  Teams had a repeatable approach for building AI systems, reducing duplicated effort and improving quality.

Tradeoffs:
  I kept standards lightweight enough for fast-moving teams while requiring stricter controls for high-risk use cases.

Playbook Structure

mindmap root((AI Engineering Playbook)) Prompting Prompt templates Structured outputs Versioning RAG Chunking Retrieval Reranking Citations Evals Agents Tool schemas State management Retries Human approval Security PII handling Prompt injection Secrets Access control Operations Logs Metrics Alerts Cost tracking Delivery CI/CD Docker Rollback Documentation

6. Metrics You Can Use in STAR Stories

Use numbers wherever possible. If you do not have exact numbers, use conservative phrasing like “approximately,” “reduced by around,” or “improved from X to Y in internal testing.”

Technical Metrics

  • Latency reduced by X%
  • Cost per task reduced by X%
  • Retrieval hit rate improved from X to Y
  • Hallucination/error rate reduced by X%
  • Tool-call success rate improved by X%
  • Regression pass rate improved to X%
  • Deployment frequency increased
  • Incident count reduced

Product/Business Metrics

  • Support tickets reduced by X%
  • Manual review time reduced by X hours/week
  • User adoption increased
  • Conversion or retention improved
  • Content generation throughput increased
  • Internal developer productivity improved

Quality Metrics

  • Faithfulness score
  • Answer relevance
  • Citation accuracy
  • Task completion rate
  • Human approval/rejection rate
  • Escalation rate

7. Mermaid Diagram: STAR Story Preparation Workflow

flowchart TD A[List Past Projects] --> B[Select 8 Strong Stories] B --> C[Map Each Story to Job Requirements] C --> D[Write STAR+T Version] D --> E[Add Metrics] E --> F[Add Architecture Diagram] F --> G[Prepare 90-Second Version] G --> H[Prepare 3-Minute Deep Dive] H --> I[Practice Follow-up Questions]

8. 90-Second Answer Formula

Use this when the interviewer asks a broad question.

One project that is relevant is [project name].
The problem was [business/technical problem].
My role was [specific ownership].
I designed/built [architecture/components].
The difficult part was [failure mode/tradeoff].
I handled it by [specific technical actions].
The result was [metric/impact].
The main lesson was [production insight].

Example

One project that is relevant is a RAG-based internal knowledge assistant.
The problem was that users had to manually search policy documents and often got incomplete answers.
My role was to design the retrieval and answer-generation pipeline.
I built ingestion, chunking, embeddings, vector search, metadata filters, grounded prompting, and citations.
The difficult part was that answers were sometimes wrong even when the data existed, so I debugged retrieval separately from generation.
I improved chunking, added metadata filtering and reranking, and created regression evals.
The result was a more reliable assistant with measurable improvements in answer quality and faster information lookup.
The main lesson was that RAG quality depends as much on retrieval and evaluation as on the LLM prompt.

9. Follow-Up Questions to Prepare for Each Story

For every STAR story, prepare answers to these:

1. What was your exact contribution?
2. What architecture did you choose and why?
3. What were the biggest failure modes?
4. How did you evaluate quality?
5. How did you handle latency and cost?
6. How did you make it secure?
7. What would you improve if you had more time?
8. What tradeoffs did you make?
9. How would you scale it?
10. What did you learn?

10. Red Flags to Avoid in STAR Answers

Avoid saying:

- I just used ChatGPT with a prompt.
- I built a demo but did not test it.
- I did not measure quality.
- I did not handle failures.
- I did not log tool calls or outputs.
- I used agents for everything.
- I fine-tuned because RAG was hard.
- I do not know how it behaved in production.

Say instead:

- I separated deterministic workflow logic from LLM reasoning.
- I added evals and traces to understand failures.
- I used guardrails for tool execution.
- I measured latency, cost, quality, and task success.
- I treated prompts as versioned artifacts.
- I designed for retries, fallbacks, and observability.

11. Quick Checklist Before Interviews

- [ ] I have 8 STAR+T stories ready.
- [ ] Each story has a metric or measurable impact.
- [ ] Each story has a technical architecture explanation.
- [ ] I can explain RAG debugging clearly.
- [ ] I can explain agent reliability clearly.
- [ ] I can explain evals and monitoring clearly.
- [ ] I can discuss LangGraph/LangChain/CrewAI at a practical level.
- [ ] I can discuss AI coding tools and MCP guardrails.
- [ ] I have one portfolio project or GitHub example to reference.
- [ ] I can answer “what would you improve?” for every story.

12. Best Story Bank for These Specific Job Descriptions

Prioritize your stories in this order:

1. Production-style RAG application
2. Agentic workflow with tool calling
3. LangGraph/state-machine orchestration
4. Evaluation and observability framework
5. Cost/latency optimization
6. AI coding tools pilot with Cursor/Claude Code/Copilot
7. MCP/context-injection setup or design
8. Engineering standards/playbooks and guardrails

If you lack direct production experience, frame personal or home projects professionally:

I built this as a production-style project, with Docker, FastAPI, evals, logging, and failure handling, to mirror how I would ship it in an enterprise/startup environment.

13. Final Positioning Statement

Use this as your high-level interview positioning:

My strength is building practical LLM systems around real engineering constraints.
I understand the model layer, but I focus heavily on the production system around it: RAG, tool calling, orchestration, state, evals, observability, guardrails, deployment, and cost/reliability tradeoffs.
I try to design AI systems that are useful, measurable, and safe to operate—not just impressive in a demo.