Geeta

Spirituality| Tech | Math | Trading | Saas|Channel : Location: Melbourne

[Geeta @TechSoulGeeta]

🚀 AI Engineering Roadmap for 2027

What Should You Learn?
Knowing how to call an LLM API won't be enough.
The next generation of AI engineers must know how to build reliable, scalable, production-ready AI systems.
🔥 8 areas worth mastering:
1Agentic AI & Multi-Agent Systems
2RAG & Knowledge Engineering
3LLMOps & Model Evaluation
4AI Data Engineering
5Multimodal AI
6Small Language Models & Edge AI
7AI Security & Governance
8AI System Architecture
💡 My prediction: The most valuable AI engineers won't be those who know the most models.
They'll be the ones who can turn AI prototypes into secure, reliable products that deliver measurable business value.
Which AI engineering skill will matter most in 2027?
#AIEngineering #ArtificialIntelligence #AgenticAI #LLMOps

[Geeta @TechSoulGeeta]

I’d start with a structured reconciliation rather than guessing the root cause.
1. Align definitions and grain
Confirm both systems use the exact same revenue definition (gross vs net, inclusions/exclusions, currency, tax treatment) and the same time window/grain. A definition mismatch is often the quickest explanation.
2. Source-to-target reconciliation by key dimensions
Pull the raw source records for the period and reconcile against both systems at a low grain (transaction ID, date, product, region, etc.).
• Identify records present in the warehouse but missing or different in finance (and vice versa).
• Check for duplicates, late-arriving data, or records filtered out by business rules in one pipeline.
• Validate timezone/timestamp handling (e.g., UTC vs local, cutoff times).
3. Trace lineage and transformations
Use data lineage tools (or manual checks of the pipelines) to inspect every transformation step between source and the two targets. Look for joins, filters, aggregations, or enrichment logic that could drop or alter ~2.5% of the revenue.
4. Quantify and isolate
Create a simple diff table:
source_amount - warehouse_amount and source_amount - finance_amount.
Group by high-cardinality dimensions to isolate where the $300K sits. Once isolated, the specific cause (timezone, duplicate, filter, late data, etc.) usually becomes obvious.
Most of the time the answer is in the reconciliation + lineage step rather than in the “successful pipeline” status.

[Geeta @TechSoulGeeta]

🔥 Data Engineering Interview Question:

Your data warehouse reports $12M in revenue.
Your finance system reports $11.7M.

Both pipelines completed successfully.
How would you identify the missing $300K?

Reconciliation? Data lineage? Time-zone differences? Duplicate transactions?
Where would you start?
#DataEngineering #DataQuality

[Geeta @TechSoulGeeta]

Temperature=0 only forces greedy decoding; it doesn’t guarantee identical outputs.
Main sources of variance I would check first:
• Model version / deployment drift (even small weight or quantisation differences)
• Non-deterministic retrieval or re-ranking in RAG (vector search, hybrid search, or metadata filters)
• Tool/API responses that themselves vary
• Inference-time effects (batching, different GPUs, floating-point non-associativity, speculative decoding)
To improve consistency I’d:
1. Pin the exact model + revision
2. Cache or freeze retrieval results for a given query
3. Make the workflow fully deterministic (fixed seeds where available, no parallel tool calls that race, structured output with constrained decoding)
4. Log the full request context (system prompt, tools, retrieval payload) so differences are visible
Truly bit-identical outputs across different machines/providers is still hard in practice, but for most product use cases you can get “consistent enough” with the controls above.

[Geeta @TechSoulGeeta]

🚀 AI Engineering Interview Question:

Your AI assistant gives different answers to the same question, even with temperature set to 0.

How would you investigate and improve consistency?

Model versions? Retrieval variability? Tool responses? Deterministic workflows?
Can LLM outputs ever be truly deterministic?
#AIEngineering #LLM

[Geeta @TechSoulGeeta]

this is a classic zero-downtime migration problem.
Safe strategy (expand-contract style):

1. Expand - Add the column as nullable (usually a fast metadata-only operation).
2. Dual-write + Backfill - Deploy app changes (via feature flag) so new writes populate the column. Simultaneously run a carefully throttled background backfill job in small batches (by primary key ranges). Monitor lag and replica load closely.
3. Verify - Confirm zero remaining NULLs.
4. Contract - Enforce NOT NULL (use online DDL tools / check-constraint tricks depending on the DB). Then clean up the feature flag and dual-write logic.
Key supporting practices:
• Rolling deployments so old and new app versions coexist
• Feature flags to control when the app starts treating the column as required
• Test the full process on a production-sized replica first
• Have a clear rollback path at every step
Avoid any single ALTER that rewrites the whole 500M-row table. The multi-phase approach keeps the system online throughout.

[Geeta @TechSoulGeeta]

💻 System Design Interview Question:

Your production database has 500 million records.
You need to add a NOT NULL column without taking the application offline.
How would you deploy this change safely?

Expand-and-contract? Backfills? Feature flags? Rolling deployments?
What's your migration strategy?
#SystemDesign #SoftwareEngineering

[Geeta @TechSoulGeeta]

I’d check FIRST: physical storage vs logical data size (especially in Delta Lake / Iceberg).
Time travel history, fail-safe retention, and unvacuumed files can easily 3x+ storage costs while row counts barely move. A recent switch from MERGE to more frequent overwrites (or missed OPTIMIZE + VACUUM) is a classic culprit here.
Then I’d look at:
• File count & average size (small files)
• Duplicates
• Compression codec
• Retention / TTL policies
Also review any recent pipeline changes.
What would you prioritise? 🔥 #DataEngineering #BigData

[Geeta @TechSoulGeeta]

🔥 Data Engineering Interview Question:

Your data pipeline processes 200 million events daily.
Suddenly, storage costs increase by 300%, but data volume grows by only 15%.

How would you investigate?
Small files? Duplicate data? Poor compression? Retention policies?
What would you check FIRST?
#DataEngineering #BigData

[Geeta @TechSoulGeeta]

Prompt guardrails alone are never enough here.

The hard limit needs to live in the tool/backend itself. When the agent calls the refund tool, the service should:
• Validate the amount against the agent’s hard policy (max $100)
• Check any per-customer or session limits
• Reject the call immediately if it exceeds the threshold
Treat every tool invocation as untrusted input, even if it came from your own agent. The LLM can be tricked; the code that actually moves money shouldn’t be.
Optional extra layers:
• Policy engine that evaluates every tool call before execution
• Require human approval above a certain threshold
• Log and alert on any attempt to exceed the limit
Enforcement belongs at the tool boundary, not inside the prompt.

[Geeta @TechSoulGeeta]

🚀 AI Engineering Interview Question:

Your AI agent is authorised to issue refunds up to $100.
A customer tricks it into processing a $5,000 refund.

How would you design the system to prevent this?
Prompt guardrails? Policy engines? Tool-level authorisation?
Where should enforcement happen?
#AgenticAI #AIEngineering

[Geeta @TechSoulGeeta]

I’d start by sampling a few hundred recent production inputs and getting fresh ground-truth labels on them (hand-labelling or via a reliable secondary process).
Then I’d score the existing model on that new labelled set.

This single step immediately tells you:
• Whether real performance has actually dropped (concept/data drift or distribution shift), or
• Whether the reported drop is mostly a measurement/evaluation gap (test set no longer representative, label noise, different traffic mix, etc.).
Once you have that signal you can decide whether to dig into feature drift stats, prediction distribution shifts, error analysis on the new failures, or pipeline/logging issues. Drift names are useful later seeing the actual changed examples first is usually faster and more reliable.

[Geeta @TechSoulGeeta]

🚀 AI Engineering Interview Question:

Your AI model scores 98% accuracy in testing.
Three months after deployment, accuracy drops to 76%.
No code changes. No model updates.
How would you identify and fix the problem?
Data drift? Concept drift? Evaluation gaps?
What would you investigate FIRST?
#AIEngineering #MLOps

[Geeta @TechSoulGeeta]

First thing I’d check: per-partition lag (https://t.co/mOUgAGySBT --describe).
If lag is concentrated on 1–2 partitions → hot key / partition skew. Adding consumers won’t help because Kafka assigns each partition to only one consumer in the group.

If lag is even across all partitions → overall throughput problem (slow processing or downstream bottleneck).
After that, look at processing time per record and downstream latency (especially DB writes). Those usually reveal the real constraint.

[Geeta @TechSoulGeeta]

🔥 Data Engineering Interview Question:

Your Kafka consumer processes 1 million events per hour.
Suddenly, consumer lag grows to 10 million messages.
Adding more consumers doesn't help.
How would you diagnose the bottleneck?
Partition limits? Slow processing? Database writes?
What would you check FIRST?
#DataEngineering #Kafka

[Geeta @TechSoulGeeta]

It is classic cache stampede / thundering herd problem.

I’d use a combination:
1. Circuit breaker around Redis (and the DB path). When Redis fails, open the circuit quickly so requests fail fast or go to a degraded path instead of hammering the DB.
2. Request coalescing / single-flight. For any given cache key, only one request is allowed to hit the DB; others wait for that result and share it. Stops the stampede cold.
3. Stale-while-revalidate + local cache. Keep a short-lived in-memory/local cache (or Redis secondary) that can serve slightly stale data. On miss or Redis down, return stale immediately and refresh in the background (or on a controlled number of threads).

Recovery strategy:
• Once Redis is healthy, half-open the circuit and ramp traffic gradually.
• Warm hot keys (either proactively or on first miss with coalescing).
• Rate-limit / shed load on the DB during the transition.
• Prefer probabilistic early expiration (or background refresh) going forward so a full Redis outage is less likely to cascade.

[Geeta @TechSoulGeeta]

💻 System Design Interview Question:

Your application uses Redis for caching.
Redis goes down during peak traffic.
Suddenly, every request hits the database, overwhelming it within seconds.
How would you prevent this cascading failure?
Circuit breakers? Request coalescing? Stale cache?
What's your recovery strategy?
#SystemDesign #SoftwareEngineering

[Geeta @TechSoulGeeta]

💻 System Design Interview Question:

Your application serves users across 10 countries.

A deployment works perfectly in Australia but fails for users in Singapore and Europe.

How would you investigate?
Regional configuration? DNS? Data residency? Network latency?
What would you check FIRST?
#SystemDesign #CloudArchitecture

[Geeta @TechSoulGeeta]

42. Arkency – https://t.co/G8nY3Sz0Ea
43. Automattic – https://t.co/Rq5dg1Ebze
44. Awesomemotive – https://t.co/xzQUYOYRem
45. Buffer – https://t.co/a1EPUSLivw
46. Constructor – https://t.co/bRY66AUdVY
47. Contra – https://t.co/0ulW1vxpI7
48. Doist – https://t.co/3qaEWjeQaE
49. DuckDuckGo – https://t.co/9Nx4JjmgIY
50. Bold – https://t.co/02juNkrWa0
51. Akamai Technologies – https://t.co/QPfHRhlSfk
52. Cloudbeds – https://t.co/hCkPnv4WTo
53. Mentorsity – https://t.co/Fv78yAvZyt
54. Expert Thinking – https://t.co/nuG9L8Av71
55. iVisa – https://t.co/SrFFiHkxbE
56. Affordmate – https://t.co/reoKlrd3j5
57. Xapobank – https://t.co/9N1yu0sUFN
58. Deltek – https://t.co/72oJpH34Sx
59. Confluent – https://t.co/7sHIEy0iMA
#RemoteJobs #Jobseekers #Career #WorkFromAnywhere #Hiring

[Geeta @TechSoulGeeta]

Companies Hiring for 100% Remote Roles

(Save this post for later!)
Here’s a clean, compiled list of companies actively hiring for fully remote positions:

1. Symetra – https://t.co/3WHSKApZLq
2. Docker, Inc – https://t.co/D66pbF9fh6
3. Cengage Group – https://t.co/cGpA6N1qDP
4. Quest Software – https://t.co/9N7wZtOWTX
5. Upstart – https://t.co/SRWyL2oN6T
6. hims & hers – https://t.co/VizJtzeqki
7. UserGems – https://t.co/cqJvJMd5Fw
8. Abnormal Security – https://t.co/FCHkJOSNRX
9. Cash App – https://t.co/emALjiCyeR
10. LogicGate – https://t.co/EWjiUlnHFY
11. Faire – https://t.co/U7o35qSRo0
12. Renaissance – https://t.co/bmNxxZk1PU
13. Rec Room – https://t.co/ZgoR2E4MSj
14. VGS – https://t.co/EL0s5Jee7I
15. VAST Data – https://t.co/BUsLUHRMJs
16. Sorcero – https://t.co/6WweNpqRus
17. McGraw Hill – https://t.co/DypIJ1uWB4
18. Patreon – https://t.co/GnX13zhJ5W
19. Beekeeper – https://t.co/VGJdHDAxdF
20. Confluent – https://t.co/7sHIEy0iMA

[Geeta @TechSoulGeeta]

21. Upwork – https://t.co/2Z2jeH3Ss7
22. DocuSign – https://t.co/L44rQ5CtCI
23. https://t.co/56lXu3qaaF – https://t.co/uLuDseERba
24. Census – https://t.co/TuHDjh5gSS
25. Intrado – https://t.co/U4VTLFwxig
26. Cloudflare – https://t.co/iCD8LbfX5x
27. https://t.co/2yo5qG3Lew
28. Workiva – https://t.co/fSjdMXnzwk
29. MissionWired – https://t.co/A7gFyulhVN
30. Workera – https://t.co/olEJZaSJJF
31. Jenius Bank – https://t.co/1dLM2QrKma
32. Goinstacare – https://t.co/iTtBxPLJ5E
33. Uplers – https://t.co/kdP2n3oKDY
34. Quantum – https://t.co/3I48ZoDMct
35. Canonical – https://t.co/weUdtlvKIg
36. DigitalOcean – https://t.co/DXHsuLAvCL
37. Atlassian – https://t.co/08t7yGeMvL
38. AngelOne – https://t.co/B7jj9OowRX
39. Shopify – https://t.co/qKZbCL9Vbb
40. Appcues – https://t.co/mUOj3Z1QG0