Technical AI Engineer Interview Analysis & Improvement Plan
This interview was for a Technical AI Engineer position focused on healthcare data integration and AI applications. The interviewer was particularly interested in understanding your current work with fraud detection using AI/ML and assessing your technical knowledge of LLMs, database technologies, and SQL.
The conversation covered several technical areas:
The interviewer's style was technically probing, seeking to understand the depth of your knowledge and the rationale behind technical decisions.
You demonstrated knowledge across multiple domains including graph databases, AWS services, and ETL processes.
You effectively explained the use of graph-based analysis for fraud detection.
You highlighted your versatility in wearing "multiple hats" across AI engineering, MLOps, and data engineering.
When challenged with a SQL problem, you correctly identified the need for GROUP BY functions or window functions.
There were several moments where explanations of technical concepts lacked precision, particularly around how LLMs are used in fraud detection.
When faced with unclear questions, you sometimes proceeded with uncertain answers rather than asking clarifying questions.
The interviewer probed for deeper technical understanding of your LLM implementation, and your responses could have been more specific.
Your technical explanations would benefit from a more structured approach (problem → solution → implementation → results).
"I'm working at Neo007, which is a startup for detecting credit card fraud... I'm an AI engineer, but I have some experience doing machine learning ops and data engineering."
"So how you can use LLMs to do credit card detection?"
"Well, it's like for real-time detection... I analyzed that using the LLM, so some graph-based algorithms..."
Key moment: The interviewer seemed confused about how LLMs were being used for structured transaction data analysis - this was an opportunity to clarify your approach more precisely.
"We have a project... for the past two or three years, we connected different hospitals in one single system... Are you familiar with all those AWS services?"
"Yeah, I've used mainly AWS environment, so either AWS Glue for the ETL pipeline... and using S3 for buckets."
Key moment: When the interviewer described their healthcare data integration project, you could have drawn more parallels to your own experience.
"Can you write me a SQL query? I want to get unique orders... and how many times the status has been changed in the past..."
"I think I would use... group functions or window functions..."
Key moment: This was an opportunity to demonstrate stronger SQL skills, which are clearly important for this role.
Practice explaining precisely how LLMs can be used in fraud detection:
"Traditional fraud detection relies on rule-based systems or classical ML models that struggle with complex patterns and relationships."
"We use a hybrid approach where graph databases (Neo4j) capture transaction relationships, while LLMs provide two key capabilities:"
"Specifically, we use LLMs to:"
"This hybrid approach improved detection accuracy by X% and reduced false positives by Y%"
Practice the SQL query that was requested in the interview:
WITH RankedOrders AS (
SELECT
order_id,
status,
timestamp,
COUNT(*) OVER (PARTITION BY order_id) as status_count,
ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY timestamp DESC) as rn
FROM orders
)
SELECT
order_id,
status as current_status,
timestamp as latest_timestamp,
status_count - 1 as previous_status_count
FROM RankedOrders
WHERE rn = 1;Practice explaining this query step by step:
"First, I create a CTE (Common Table Expression) called RankedOrders"
"Within this CTE, I use window functions to count the total number of status records for each order"
"I also use ROW_NUMBER() to identify the most recent status update for each order"
"Finally, I select only the most recent status record for each order (WHERE rn = 1)"
"I subtract 1 from the status_count to get the number of previous statuses"
Practice asking clarifying questions when faced with unclear technical inquiries:
Potential clarifying questions:
Practice the STAR method for explaining your technical projects:
"At Neo007, we needed to improve fraud detection accuracy while reducing false positives."
"My responsibility was to design and implement a system that could identify complex fraud patterns by analyzing relationships between transactions and entities."
"I implemented a hybrid architecture using:
"This system improved detection accuracy by 30%, reduced false positives by 25%, and enabled analysts to investigate suspicious transactions 40% faster through natural language interfaces."
This interview was heavily focused on technical depth and precision, particularly around how AI/ML technologies are applied to real-world problems.
The interviewer seemed particularly interested in understanding the rationale behind technical decisions (e.g., why use LLMs for fraud detection) rather than just implementation details.
SQL skills are clearly important for this role, particularly for the healthcare data integration project.
For future interviews in similar roles, focus on: