Jll Software Engineer Interview Guide

The 60-second brief

A Software Engineer at JLL (Jones Lang LaSalle) plays a pivotal role in transforming the commercial real estate industry through cutting-edge technology. Working within high-impact divisions like the GenAI and Central Platform teams, engineers at JLL build, scale, and maintain the digital infrastructure that powers global real estate services. This includes designing sophisticated backend microservices, optimizing complex search and retrieval engines, and implementing state-of-the-art Retrieval-Augmented Generation (RAG) systems to drive intelligent assistant applications. The impact of this role is immense, directly affecting how JLL clients and internal brokers navigate vast datasets to make multi-million-dollar real estate decisions.

This guide is scoped to a Software Engineer candidate at Jll.

Focus areas

• Python
• RAG (Retrieval-Augmented Generation)
• Search & Retrieval Systems

The Jll interview process

Jll candidates report 3 rounds over 3-5 weeks. The stages below are what candidates describe, not a published process.

Initial Technical Screening

Conducted over the phone or via video to assess basic technical skills.

What to demonstrate

• Conducted over the phone or via video to assess basic technical skills
• Depth in Python

How to prepare

• Answer aloud and timed: How do you design and secure a RESTful API using Node.js or Python in an Azure cloud environment?
• Answer aloud and timed: Describe your experience implementing microservices. How do you handle service-to-service communication and distribute transactions safely?

Deeper Technical Evaluations

Includes live coding, system design discussions, and a panel session with senior engineers.

What to demonstrate

• Includes live coding, system design discussions, and a panel session with senior engineers
• Depth in Python

How to prepare

• Answer aloud and timed: How would you optimize database queries and data modeling in a hybrid environment utilizing both SQL Server and CosmosDB?
• Answer aloud and timed: Explain how you set up automated logging, error handling, and monitoring for a mission-critical backend service.

Face-to-Face Round

May occur at a client office to evaluate client-facing communication and stakeholder management skills.

What to demonstrate

• May occur at a client office to evaluate client-facing communication and stakeholder management skills
• Depth in Python

How to prepare

• Answer aloud and timed: What is your approach to designing and optimizing a Retrieval-Augmented Generation (RAG) pipeline?
• Answer aloud and timed: How do vector databases differ from traditional relational databases, and when would you choose one over the other?

Where candidates lose points

Prepare for Panel Interviews

You may face panels of up to 5 interviewers during your virtual or on-site rounds. Practice explaining your technical decisions clearly and concisely to a group, making sure to engage with each interviewer.

Brush Up on RAG and Search Optimization

Given JLL's focus on intelligent assistants, be ready to discuss how you would design, optimize, and scale search retrieval systems and LLM integrations.

Not asking what the system looks like if it dies halfway through

For any multi-step write, say what state remains if the process stops between step two and step three, and what brings it back: a single transaction, a saga with compensating actions, an outbox, or a reconciliation job. Partial failure is routine at any real call volume, so 'that shouldn't happen' is an answer with nothing behind it.

Questions & practice

Describe a scenario where you had to improve search performance or relevance in an information retrieval syste

Medium · AI, Search, & Information Retrieval

Describe a scenario where you had to improve search performance or relevance in an information retrieval system.

Approach

1. Restate the input: its shape, its size, and what is guaranteed about it.
2. Name the brute-force solution and its complexity before improving on it.
3. Choose the data structure from the access pattern, not from familiarity.
4. State the target complexity and say which constraint rules the naive version out.

Follow-up

• How does this change if the input no longer fits in memory?
• What is the worst case, and how likely is it on real data?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

1. One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
2. Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
3. Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
4. If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

• The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
• How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

1. Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
2. For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
3. For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
4. Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

• A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
• Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

How would you optimize database queries and data modeling in a hybrid environment utilizing both SQL Server an

Medium · Backend & Cloud Architecture

How would you optimize database queries and data modeling in a hybrid environment utilizing both SQL Server and CosmosDB?

Approach

1. Name the grain you start from and join outward from it.
2. Check whether any join is one-to-many before aggregating, or the sums inflate.
3. Say which index the query would use, and what makes it unusable.
4. Handle the rows that do not match: that is usually the actual question.

Follow-up

• How does the query change if that join becomes one-to-many?
• What happens to this when the table is ten times larger?

Hold a per-tenant active cap against concurrent creates

Hard · write skew · isolation levels · constraints

A tenant on the standard plan may hold at most 50 resources with status='active'. The create handler runs SELECT count(*) FROM resource WHERE tenant_id = $1 AND status = 'active', compares to 50, then inserts. Two creates arrive 3 ms apart on different instances and the tenant lands at 51. Name the anomaly, say whether PostgreSQL 16 READ COMMITTED or REPEATABLE READ prevents it and why, then give an implementation that holds the cap at READ COMMITTED with the exact statements. Finally, say what changes when the cap is 'at most one running export per tenant' on job_run.

Approach

1. Name it: write skew. The two transactions read an overlapping set and write disjoint rows, so there is no row-level conflict for the engine to detect and each commit is individually legal.
2. Rule out the levels precisely. READ COMMITTED takes a fresh snapshot per statement and takes no lock on the counted rows, so both see 49. PostgreSQL's REPEATABLE READ is snapshot isolation: it removes non-repeatable reads and phantoms within the snapshot but still admits write skew, because the anomaly is not a re-read of a changed row, it is a read of a set that a concurrent transaction invalidates. Only SERIALIZABLE closes it, by tracking the read dependency and aborting one transaction with SQLSTATE 40001 — a guarantee that exists only if the application re-runs the whole transaction from the read.
3. Convert the set predicate into a single-row conflict: keep tenant.active_resource_count and run UPDATE tenant SET active_resource_count = active_resource_count + 1 WHERE tenant_id = $1 AND active_resource_count < 50 in the same transaction as the INSERT. Zero affected rows is the cap, returned as 409. The row lock serialises the decision at any isolation level, and contention is bounded to one tenant's row — which is also the fair-scheduling unit, unlike a global counter that would convoy every tenant behind one row.
4. State the cost you just took on: a counter is a second source of truth that can drift, so every path that changes status must adjust it inside the same transaction, and a periodic reconciliation has to exist, with resource_revision as the authority for what the count should have been.

Follow-up

• A resource moves from archived back to active. Which statements change, and what breaks if the counter update and the status change land in different transactions?
• The cap becomes plan-dependent and a plan can change mid-month. Where does the number 50 live, and who reads it?

How do you design and secure a RESTful API using Node.js or Python in an Azure cloud environment?

Medium · Backend & Cloud Architecture

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

Explain how you set up automated logging, error handling, and monitoring for a mission-critical backend servic

Medium · Backend & Cloud Architecture

Explain how you set up automated logging, error handling, and monitoring for a mission-critical backend service.

Approach

1. Fix the scope first: who calls this, how often, and what they do when it fails.
2. Name the read and write paths separately; they rarely have the same bottleneck.
3. Choose a partition key and say what query it makes expensive.
4. State the consistency you need, and where you are willing to be stale.

Follow-up

• What breaks first when traffic grows ten times?
• How does this behave when that dependency is down for an hour?

What is your approach to designing and optimizing a Retrieval-Augmented Generation (RAG) pipeline?

Medium · AI, Search, & Information Retrieval

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

How do vector databases differ from traditional relational databases, and when would you choose one over the o

Medium · AI, Search, & Information Retrieval

How do vector databases differ from traditional relational databases, and when would you choose one over the other?

Approach

1. Clarify what is being asked and what a complete answer contains.
2. State your assumptions explicitly before working the problem.
3. Say what you would check first and why it is the highest-information step.
4. Work from the requirement backwards to the design.

Follow-up

• What assumption would you test first?
• How would you know your answer was wrong?

Exports duplicate a row range about once a week

Hard · leases · clock skew · idempotency

Roughly once a week an export writes a file containing a duplicated range of rows. The affected job_run rows show attempt = 1, status = succeeded, one started_at, and a lease_owner naming a different host from the one whose logs show the job starting. Leases last 30 seconds and are heartbeated every 10 from inside the handler; lease_expires_at is computed on the worker and compared against the database's now(). Find the mechanism, and give a fix that holds even if you cannot fix the clocks.

Approach

1. Start from the fact that eliminates the obvious answer. attempt = 1 means no retry was recorded, so this is not a re-run after failure; two workers ran the same row concurrently and the takeover path never touched the counter. lease_owner naming a host other than the one that started the job is the same statement from the other side.
2. Enumerate the mechanisms that cause a premature takeover, then find the signal that separates them. Either the lease genuinely expired because the heartbeat did not fire, which is what happens when the heartbeat runs on the handler's own thread and the handler makes a long blocking call, or it only appeared expired because two clocks disagree, since lease_expires_at is written from the worker's clock and evaluated against the database's. The discriminator is the distribution: incidents clustered on the longest exports indict the heartbeat, incidents clustered on one host indict skew. Measure both, and measure each host's offset against the database directly.
3. Read the reclaim query precisely. In PostgreSQL now() is transaction start time, not statement time, so a reclaimer holding a long transaction compares against an older timestamp than expected; clock_timestamp() is the statement-time function. This is worth ruling in or out before you redesign anything, because it changes which rows look expired.
4. Remove the second clock rather than trying to synchronise it. Issue and extend the lease in the database, with lease_expires_at = now() + interval '30 seconds' in both the claim and the heartbeat, so exactly one clock is ever compared and worker skew stops mattering to this predicate.

Follow-up

• The displaced worker has already streamed half the file to object storage. What makes that side effect safe to repeat?
• You now count takeovers. What alert fires on that counter, and at what threshold?

Suggested practice checklist

Built from the rounds and topics Jll candidates report.

Day 1 — Map the Jll loop

• Write out the reported sequence: Initial Technical Screening, Deeper Technical Evaluations, Face-to-Face Round.
• For each round, write one sentence on what it is judging, from the description above, and mark the one you are least ready for.

Deliverable: A one-page map of the 3 reported rounds, with the weakest marked.

Day 2 — Work Python

• Spend the session on Python, which Jll candidates report being tested on.
• Write one worked example in Python and time yourself on it.

Deliverable: One timed worked example in Python.

Day 3 — Work RAG (Retrieval-Augmented Generation)

• Spend the session on RAG (Retrieval-Augmented Generation), which Jll candidates report being tested on.
• Write one worked example in RAG (Retrieval-Augmented Generation) and time yourself on it.

Deliverable: One timed worked example in RAG (Retrieval-Augmented Generation).

Day 4 — Work Search & Retrieval Systems

• Spend the session on Search & Retrieval Systems, which Jll candidates report being tested on.
• Write one worked example in Search & Retrieval Systems and time yourself on it.

Deliverable: One timed worked example in Search & Retrieval Systems.

Day 5 — Answer out loud: Backend & Cloud Architecture

• Answer aloud, timed: How do you design and secure a RESTful API using Node.js or Python in an Azure cloud environment?
• Answer aloud, timed: Describe your experience implementing microservices. How do you handle service-to-service communication and distribute transactions safely?

Deliverable: Spoken answers to 2 reported Backend & Cloud Architecture question(s), under time.

Day 6 — Answer out loud: AI, Search, & Information Retrieval

• Answer aloud, timed: What is your approach to designing and optimizing a Retrieval-Augmented Generation (RAG) pipeline?
• Answer aloud, timed: How do vector databases differ from traditional relational databases, and when would you choose one over the other?

Deliverable: Spoken answers to 2 reported AI, Search, & Information Retrieval question(s), under time.

Day 7 — Answer out loud: Behavioral & Client Collaboration

• Answer aloud, timed: Tell me about a time you had to translate complex technical requirements for a non-technical client or business stakeholder.
• Answer aloud, timed: Describe a situation where you had to adapt quickly to shifting project priorities. How did you manage your deliverables?

Deliverable: Spoken answers to 2 reported Behavioral & Client Collaboration question(s), under time.

Behavioural preparation

Behavioural rounds judge the decision you made and what it cost.

Describe your experience implementing microservices. How do you handle service-to-service communication and di

Medium · Backend & Cloud Architecture

Describe your experience implementing microservices. How do you handle service-to-service communication and distribute transactions safely?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you handle prompt engineering and response validation when integrating LLMs into enterprise application

Medium · AI, Search, & Information Retrieval

How do you handle prompt engineering and response validation when integrating LLMs into enterprise applications?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Tell me about a time you had to translate complex technical requirements for a non-technical client or busines

Medium · Behavioral & Client Collaboration

Tell me about a time you had to translate complex technical requirements for a non-technical client or business stakeholder.

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Describe a situation where you had to adapt quickly to shifting project priorities. How did you manage your de

Medium · Behavioral & Client Collaboration

Describe a situation where you had to adapt quickly to shifting project priorities. How did you manage your deliverables?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

How do you handle disagreements during a code review, especially when advocating for a specific architectural

Medium · Behavioral & Client Collaboration

How do you handle disagreements during a code review, especially when advocating for a specific architectural design?

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?

Give an example of how you mentored a junior engineer or guided a team through a difficult technical bottlenec

Medium · Behavioral & Client Collaboration

Give an example of how you mentored a junior engineer or guided a team through a difficult technical bottleneck.

Approach

1. Pick a story where you made the decision, not one where you watched it.
2. State the situation in two sentences and spend the rest on the reasoning.
3. Give the blast radius: what could have broken, and what you measured.
4. Name the disagreement and how you resolved it with evidence.

Follow-up

• What would you do differently if you ran that again?
• How did you know your change caused the improvement?