Agoda Engineering Manager Back End LLM Interview Prep Guide

Agoda Engineering Manager Back End LLM Interview Prep Guide | DevMentor

What You're Actually Walking Into#

Let me be straight with you: Agoda's Engineering Manager interview for Back End – LLM roles is one of the more demanding EM loops in Southeast Asia's tech scene. It's not just a people-manager check. They want someone who can own the technical direction of teams building systems that interact with Large Language Models at scale — think prompt pipelines, RAG architectures, inference infrastructure, and the engineers who build them.

I've coached candidates through this process, and the ones who struggle most are the ones who prep like it's a generic EM interview. It's not. Agoda has specific expectations, specific formats, and a very particular culture bar. Let's break it all down.

The Interview Process: Round-by-Round Breakdown#

Agoda's interview process for senior engineering roles typically spans 3–5 weeks. Here's the typical round order you should expect:

Round	Format	Duration	Primary Focus
1	Recruiter Screen	30 min	Role fit, compensation, logistics
2	Technical Phone Screen	45–60 min	Back-end fundamentals, LLM system awareness
3	Take-Home Assignment	3–5 days	System design, architecture thinking
4	Take-Home Presentation	60 min	Communication, depth, trade-offs
5	Leadership & Behavioral Panel	60–90 min	People management, conflict, culture
6	Senior Leadership / Bar Raiser	45–60 min	Values alignment, strategic thinking

Let's walk through each one.

Round 1: The Recruiter Screen#

This isn't a throwaway call. Agoda's recruiters are well-briefed on technical expectations. They're checking three things:

Scope match: Have you managed backend engineering teams of 6+ engineers? Have you shipped production systems?
LLM familiarity: Not expecting you to have trained GPT-4, but they'll probe whether you understand what it means to build around LLMs — inference latency, cost per token, prompt versioning, hallucination mitigation.
Motivation: Why Agoda, why now? "It's a great company" won't cut it.

What to say: Come prepared with a tight 2-minute pitch: your current scope, team size, a recent technical win, and one specific reason you're excited about Agoda's LLM product direction. Mention their AI-powered travel personalization work — it shows you've done homework.

Round 2: Technical Phone Screen#

Here's what most EM candidates get wrong — they assume the technical screen is light because they're interviewing for a manager role. At Agoda, it's not.

The interviewer is checking whether you can hold your own in a technical conversation with the senior engineers on your team. For Back End – LLM, expect questions like:

"Walk me through how you'd design a retrieval-augmented generation (RAG) pipeline that serves 10,000 queries per second."
"How would you approach latency budgeting when your back end is calling an external LLM API?"
"What are the trade-offs between fine-tuning a model vs. prompt engineering at scale?"

You don't need to code. But you need to think out loud with precision.

How to talk through it: Start like this — "Before I dive in, let me clarify the constraints. Are we optimizing for cost, latency, or accuracy here? And is the LLM provider external like OpenAI, or an internal model?" That kind of question immediately signals senior-level thinking.

Round 3: The Take-Home Assignment#

This is Agoda's version of a deep-dive design challenge. You'll typically get 3–5 days to complete it. The prompt varies, but for Back End – LLM roles you might see something like:

"Design a back-end system that allows Agoda's customer service agents to query a knowledge base using natural language. The system must handle 5,000 concurrent users, support multi-language queries, and integrate with an existing PostgreSQL data store."

Deliverables They Expect#

A written design document (think RFC/ADR style — 4–8 pages)
An architecture diagram (clear boxes, arrows, and labels)
A section on trade-offs and decisions you didn't make and why
Optional but impressive: a rough cost estimate or capacity model

Scope Strategy#

Here's the thing most people miss: they're not expecting a perfect system. They're evaluating how you think about scope under constraints. Be explicit. Say "In the interest of time, I'm not covering auth and rate limiting in depth, but here's how I'd approach it at a high level." That shows maturity.

Here's a sample pseudocode sketch of a RAG pipeline you might reference in your write-up:

python

# Simplified RAG pipeline for customer query handling
def handle_query(user_query: str, user_language: str) -> str:
    # Step 1: Translate to English if needed
    normalized_query = translate_to_english(user_query, source_lang=user_language)
 
    # Step 2: Embed the query
    query_embedding = embedding_model.encode(normalized_query)
 
    # Step 3: Retrieve top-k relevant documents from vector store
    top_docs = vector_store.similarity_search(
        query_embedding,
        top_k=5,
        filters={"source": "knowledge_base"}
    )
 
    # Step 4: Build prompt with context
    prompt =

Including something like this — not as the answer, but as a conversation starter — shows you can bridge management and technical depth.

Round 4: The Take-Home Presentation#

This is a 60-minute live session where you present your take-home to a panel of 2–4 engineers and possibly a senior EM. The first 20–25 minutes are yours. The rest is Q&A.

Presentation Expectations#

Don't read your doc. They've read it. Narrate the thinking, not the output.
Open with the problem framing: "The core challenge here was balancing latency SLAs with the unpredictability of LLM response times. Everything I designed flows from that tension."
Be ready to go deep on any single component. They will pick one box on your diagram and say "walk me through this in detail."
Expect a "what would you change with 6 more months?" question. Have an honest answer.

A common trap: Candidates get defensive when the panel challenges their design. Don't. The right response to "why didn't you use a streaming response architecture?" is "Great point — I considered it. Here's why I deprioritized it, but I'd absolutely revisit that if latency data showed it was a bottleneck."

Round 5: Leadership & Behavioral Panel#

This is where the EM-specific evaluation really happens. Agoda uses a structured behavioral format — expect STAR-format questions, but they probe hard on the A (Action) and R (Result).

Common questions you'll face:

"Tell me about a time you had to deliver bad news to a senior stakeholder about a project delay."
"How have you handled an underperforming engineer on your team?"
"Describe how you've influenced technical direction without direct authority."
"Tell me about a time your team shipped something that failed in production. What did you do?"

What They're Really Checking#

For an LLM-adjacent role, they want to see that you can manage engineers working in an ambiguous, fast-moving space. LLM tooling changes monthly. Can you help your team navigate that without chaos?

Here's a phrasing template that works well:

"When my team was evaluating which vector database to adopt, there were three viable options and no clear industry standard. I ran a structured spike — one engineer per option, two-week timebox, shared evaluation rubric. We converged on [X] because of [specific reason tied to our constraints]. The key was making the decision process transparent so the team trusted the outcome even if they'd championed a different option."

That answer shows: structured decision-making, team trust, constraint-driven thinking. That's the signal.

Round 6: Senior Leadership / Bar Raiser#

Agoda's final round often involves a very senior leader — sometimes the VP of Engineering or a principal engineer acting as a culture and quality bar holder. Think of this as Agoda's version of Amazon's Bar Raiser concept.

They're not re-testing technical depth. They're asking:

Does this person think at the right altitude?
Do they have opinions grounded in data?
Will they raise the bar for the teams around them?

Expect abstract questions like:

"What's your philosophy on when to build vs. buy for AI/ML infrastructure?"
"How do you think about the career development of engineers who specialize in LLM tooling — a space that might look very different in 2 years?"

Here's a sample answer structure for the build vs. buy question:

Framework: Build vs. Buy Decision Matrix

- Competitive differentiator? → Build
- Commodity with strong OSS/vendor options? → Buy/integrate
- Speed-to-market critical? → Buy, plan migration path
- Data privacy constraints? → Build or self-host
- Team has expertise to maintain? → Build sustainably
- Maintenance cost > value over 2 years? → Buy

Presenting a mental model like this — even verbally — signals executive-level thinking.

Where Candidates Get Stuck#

Treating this like a pure people-manager interview. You need technical credibility and leadership depth. Don't coast on "I delegate the technical decisions to my team."
Underselling the take-home. This is your biggest differentiator. Candidates who submit a clean, well-reasoned doc stand out dramatically. Spend real time on it.
Blanking on LLM specifics. You don't need to be an ML engineer. But not knowing what a token is, or what RAG stands for, is a red flag for this specific role.
Defensiveness during the presentation Q&A. Every challenge is an invitation to think out loud together. Welcome it.
Generic behavioral answers. "We worked as a team" with no specifics kills credibility. Nail your STAR stories with precise numbers and outcomes.

How to Talk Through This in an Interview#

Here's example dialogue for the technical phone screen:

Interviewer: "How would you ensure your back-end LLM service meets a 95th-percentile latency SLA of 2 seconds?"

You: "Good question — let me think through this. First, I'd want to understand where the latency budget is being spent. LLM inference is typically the dominant cost, so I'd start by profiling that. Options include: caching frequent queries — semantic caching if we're okay with approximate matches — streaming responses to improve perceived latency, and circuit-breaker patterns so we fail fast and serve cached or degraded responses rather than timing out. At the infrastructure level, I'd look at co-locating the inference service to minimize network hops. I'd also want alerting on p95 and p99 latency separately so we're not just optimizing the median. Does that align with the constraints you're working with, or is there a specific part of the pipeline causing the most pain?"

That response shows problem decomposition, practical solutions, and collaborative instinct — all in under 90 seconds.

Your Preparation Timeline#

4 Weeks Out:

Study RAG pipelines, vector databases (Pinecone, Weaviate, pgvector), and LLM API integration patterns
Prep 8–10 STAR stories covering: delivery, conflict, underperformance, technical decisions, stakeholder management
Review Agoda's engineering blog and LinkedIn for recent LLM/AI product announcements

2 Weeks Out:

Practice system design out loud — record yourself and review
Draft a sample design doc for a hypothetical LLM feature (great take-home prep)
Mock behavioral interviews with a peer or coach

1 Week Out:

Refine your "why Agoda" narrative
Prepare 3–5 smart questions for each round
Do a dry run of your take-home presentation format

Practice Resources:

System design: bytebytego.com, Designing Data-Intensive Applications (Kleppmann)
LLM engineering concepts: LangChain docs, LlamaIndex docs
Behavioral prep: Lenny's Newsletter EM interviews, "The Manager's Path" by Camille Fournier
Mock interviews: Pramp, Interviewing.io, or DevMentor coaching sessions

Red Flags to Avoid#

Saying "I trust my team to handle the technical details" when asked about LLM architecture
Submitting a take-home that's all text and no diagrams
Not having concrete metrics in your behavioral answers ("we improved performance" vs. "we reduced p99 latency by 40%")
Asking no questions at the end of any round — it signals low curiosity
Badmouthing previous employers, even subtly

Key Takeaways#

Agoda's EM – Back End LLM process is 6 rounds over 3–5 weeks. Plan your energy and prep accordingly.
The take-home is your biggest opportunity to stand out. Treat it like a real RFC, not a homework assignment.
Technical credibility matters even at the EM level. Know your RAG, know your latency budgeting, know your trade-offs.
Behavioral stories need numbers and ownership. Vague answers sink candidates who are otherwise strong.
The Bar Raiser round is about altitude, not depth. Show that you think about engineering at the org level, not just the team level.
Curiosity and collaboration are the culture signals. Ask questions, welcome challenges, and show you're a learner — especially in the fast-moving LLM space.

You've got this. Go in prepared, stay curious, and remember: they're not trying to trick you — they're trying to see if you're the person they want building the future of their AI infrastructure.