Handshake AI Engineer Interview Experience
Handshake · Senior
Interview process
The overall interview process was well organized and gave me a good opportunity to demonstrate both my technical skills and my experience with AI engineering. I felt that I did especially well when discussing Python, machine learning, LLMs, RAG, APIs, and how I approach debugging and solving technical problems. I was also comfortable explaining projects I had worked on and the decisions I made during development.
The more challenging part was answering some of the deeper technical questions under time pressure. There were a few questions where I knew the concept but could have explained my thought process more clearly or provided a stronger example. Overall, though, the experience was positive and helped me identify a few technical areas I want to continue strengthening.
Interview rounds · 3
- 1
Recruiter screen
TechnicalArtificial IntelligenceCross-FunctionalMachine LearningDebuggingCode ReviewCodingSystem DesignQ1. Tell me about yourself and your background in AI/ML.
How they answeredI have a background in software engineering and AI/ML, with most of my hands-on experience centered around Python, backend development, APIs, generative AI, and model evaluation. I've worked with machine learning frameworks such as PyTorch, TensorFlow, and scikit-learn, as well as LLM tools and frameworks like LangChain and LlamaIndex. My recent work has involved building and evaluating AI applications, working with RAG pipelines, prompt engineering, and testing model responses for accuracy and reliability.
Q2. Walk me through your most recent AI engineering work.
How they answeredRecently, I've been working on AI systems involving retrieval, evaluation, and model testing. I've built RAG workflows where documents are processed, chunked, embedded, stored in a vector database, and retrieved based on user queries. I've also spent a lot of time evaluating model outputs, identifying failure cases, improving prompts, and debugging issues in AI pipelines. My work usually involves Python, APIs, vector databases, and testing to make sure the final system is reliable rather than just working on the ideal cases.
Q3. Why are you interested in this AI Engineer position?
How they answeredI'm interested in the position because it combines software engineering with applied AI, which is where I want to continue growing. I enjoy taking AI beyond experimentation and actually building systems that solve real problems. I'm especially interested in opportunities involving LLMs, RAG, agents, evaluation, and backend development because they align closely with the work I've been doing and the technical direction I want to continue pursuing.
- 2
Online assessment
CodingTechnicalData Structures & AlgorithmsDebuggingCode ReviewQ1. Write a Python function to process and clean a dataset.
How they answeredimport pandas as pd
def clean_dataset(df):
df = df.copy()
# Remove duplicate rows
df = df.drop_duplicates()
# Remove completely empty rows
df = df.dropna(how="all")
# Normalize column names
df.columns = (
df.columns
.str.strip()
.str.lower()
.str.replace(" ", "_")
)
# Clean string columns
for column in df.select_dtypes(include="object").columns:
df[column] = df[column].str.strip()
return df
Q2. What safeguards would you add before allowing an agent to perform actions?
How they answeredI would use least-privilege tool permissions, strict input validation, authentication and authorization checks, and allowlisted actions. For sensitive operations, I would require human confirmation before execution. I would also validate the agent's proposed tool arguments instead of trusting the model output directly. On top of that, I would add rate limits, execution limits, audit logging, timeouts, and monitoring so an agent cannot repeatedly perform an action or operate outside its intended scope.
Q3. How would you implement retries without causing duplicate operations?
How they answeredI would first determine whether the operation is idempotent. For operations that create or modify data, I would use an idempotency key or unique request identifier so the server can recognize repeated requests and return the original result instead of performing the operation twice. I would combine that with limited retries, exponential backoff, jitter, and clear timeout handling. I would only retry errors that are actually transient, such as timeouts, 429 responses, or certain 5xx errors.
Q4. Explain the difference between a list, tuple, set, and dictionary in Python.
How they answeredA list is ordered and mutable, so I use it when I need a collection that can change and where order matters. A tuple is also ordered, but it's immutable, so it's useful for values that shouldn't change. A set stores unique values and is useful for removing duplicates or doing fast membership checks. A dictionary stores key-value pairs and is what I normally use when I need to associate one piece of information with another.
Q5. Write an asynchronous Python function that calls multiple APIs.
How they answeredimport asyncio
import httpx
async def fetch(client, url):
response = await client.get(url, timeout=10.0)
response.raise_for_status()
return response.json()
async def fetch_all(urls):
async with httpx.AsyncClient() as client:
tasks = [fetch(client, url) for url in urls]
results = await asyncio.gather(
*tasks,
return_exceptions=True
)
return results
- 3
Technical round
CodingSystem DesignTechnicalData Structures & AlgorithmsArtificial IntelligenceCross-FunctionalMachine LearningDebuggingCode ReviewQ1. How do you debug an API when the logs are incomplete or contradictory?
How they answeredI start with what I can verify instead of immediately trusting the error message. I reproduce the request if possible and trace it through the endpoint, service layer, database, and any external APIs. I compare timestamps, request IDs, status codes, payloads, and the last successful execution. If the logs are incomplete, I'll add targeted instrumentation around the boundary where the behavior becomes inconsistent. I try to change one thing at a time so I can prove which component is actually responsible instead of fixing several possible causes at once.
Q2. Explain RAG from ingestion through answer generation.
How they answeredI think of RAG as two main pipelines: ingestion and retrieval/generation. During ingestion, I load the source documents, clean and split them into meaningful chunks, attach metadata, generate embeddings, and store those embeddings in a vector database. When a user submits a question, I embed the query and retrieve the most relevant chunks. Depending on the application, I may apply metadata filtering, hybrid search, or reranking. I then provide the best retrieved context to the LLM with instructions to answer from that evidence. I also evaluate retrieval and generation separately because a bad answer can come from either poor retrieval or poor generation.
Q3. Walk me through an AI/ML system you built from end to end.
How they answeredOne system I worked on was a document question-answering application using RAG. I handled document ingestion and preprocessing, split the content into chunks, generated embeddings, and stored them in a vector database. I then built the retrieval layer to find relevant context for each question and connected that context to an LLM through a backend API. After getting the basic pipeline working, I created test questions and evaluated retrieval relevance, groundedness, and answer quality. I used the failures to adjust chunking, retrieval parameters, prompts, and error handling rather than relying on a few successful examples.
Q4. How would you prevent an agent from entering an infinite loop?
How they answeredI wouldn't rely on the model itself to decide when it has run too long. I would enforce a maximum number of steps or tool calls at the application level and set execution timeouts. I would also track recent actions so the system can detect repeated tool calls with the same arguments or repeated states without meaningful progress. If that happens, I would stop execution and either return a controlled failure or escalate for human review. For higher-risk agents, I would also use explicit state transitions so the model can't freely loop between tools.
Q5. How would you prevent PII from reaching an external model?
How they answeredI would treat that as a system-level control rather than relying only on the prompt. Before anything is sent to the external model, I would run the input through a detection and redaction layer for information such as names, email addresses, phone numbers, account identifiers, and other sensitive fields. Where possible, I would replace those values with placeholders or internal IDs and restore them only after processing when necessary. I would also minimize what data is sent, restrict logging of sensitive payloads, encrypt data in transit, control access, and make sure the model provider and data-retention configuration meet the application's privacy requirements.
Q6. How do you communicate technical tradeoffs to non-technical stakeholders?
How they answeredI focus on the impact of the decision instead of overwhelming people with implementation details. For example, instead of only saying that one model has lower latency, I would explain that it gives users faster responses and costs less to operate, but we may give up some accuracy on more difficult requests. I usually present the available options, the benefits and risks of each, my recommendation, and why I'm recommending it. That gives stakeholders enough information to make a decision while still keeping the technical reasoning clear.
Tips from the candidate
I would tell a friend to prepare for both technical and real-world problem-solving questions. I would recommend reviewing Python, machine learning fundamentals, LLMs, RAG, APIs, prompt engineering, model evaluation, and debugging. I would also practice explaining past projects clearly, especially the challenges faced, decisions made, and results. Most importantly, be ready to explain your thought process instead of just giving the final answer.
Company culture
What stood out to me was how collaborative and technically focused the team seemed. The people I spoke with were knowledgeable, approachable, and genuinely interested in understanding how I solve problems rather than just whether I knew the right answer. I also liked the emphasis on learning, open communication, and building practical AI solutions as a team.