SDE & AI @Amazon | Simplifying software engineering, system design, and generative AI | Building Hungry Minds: the newsletter to stay ahead in software 👇

Tell me about the last time you felt growing as a software engineer. Really.

𝙽𝚞𝚕𝚕𝙿𝚘𝚒𝚗𝚝𝚎𝚛𝙴𝚡𝚌𝚎𝚙𝚝𝚒𝚘𝚗? Here's why:

We all face information overload as software engineers. 
There's too much content and too little time.
You have to stay relevant, but it's hard.

Right now, you're either working or taking a break.

You are hoping to get a dopamine shot from scrolling your feed.

You can find:

💡 A practical tip to implement
📚 An inspirational quote
😆 Something funny

And then what?

→ Scroll past it? 
→ Forward it blindly to a friend?
→ Forget about it in the bookmarks?

The harsh truth: nothing.

What you consume passively doesn't make you learn.

How do I know?
It was the same for me for years.

How do you stay updated with AI, stay relevant in the job market, and continuously grow?

That's the problem I set out to solve with Hungry Minds - a free weekly newsletter to help software engineers learn and stay updated, without the noise.

I'm doing that by:

1. Writing to you daily on LinkedIn to 40K+ followers and over 5M impressions last year
2. Compressing 1 week of software deep dives and trends in 1 free newsletter issue
3. Sharing my thoughts and learnings being promoted to SDE II in 1.5y at Amazon

If that sounds interesting, check out Hungry Minds or DM me! I'm always down for a chat.

Save 50+ hours of your week here: 𝗵𝘁𝘁𝗽𝘀://𝗵𝘂𝗻𝗴𝗿𝘆𝗺𝗶𝗻𝗱𝘀.𝗱𝗲𝘃 (6K+ engineers from startups to big tech are reading already)

DISCLAIMER: Opinions expressed are solely my own and do not express the views or opinions of my employer.

My career background:

I'm currently an L5 software engineer at Amazon in Madrid, working on core Search CX and Personalization. 
In 2022/2023, I delivered 2 successful billion-scale projects with multiple teams across different time zones.

I have worked with multiple code languages (although this doesn't make a difference), and with tech stack in the lines of AWS CDK, React applications, scheduling systems, backends with GraphQL, and simple AI applications.

My education background:

- I finished a Master's in Machine Learning and AI with l'Ecole Polytechnique and IPP
- I completed a Master's in Computer Science and Data Science with ESILV Paris
- I got a Master's in business development and marketing with EMLV Paris

Alexandre Zajac

𝗖𝗼𝗱𝗲 𝗱𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗡𝗢𝗧 𝗔𝗯𝗼𝘂𝘁:


→ One senior gate-keeping all the knowledge. 

Code documentation is ALL about:

→ Sharing knowledge so the whole team knows how the system works.

1-person docs = single point of failure.

Whole-team docs = redundancy + knowledge multiplication.

Remember:
You NEED documentation to manage a system through people and time.


How do you manage docs effectively?


P.S: Documente my personal interview cheatsheet: https://lnkd.in/d_eCGEJR

SDE & AI @Amazon | Building Hungry Minds to 1M+ | Daily Posts on Software Engineering, System Design, and AI ⚡

No alternative text description for this image

10+1 System Design Patterns Every Engineer Should Know in 2026:


___
btw, my personal interview cheatsheet: https://lnkd.in/d_eCGEJR
___


0.💡Load Balancer: 

Distributes incoming traffic across multiple servers to ensure no single server gets overwhelmed.

➡️ Useful when: You're scaling web applications or ensuring high availability.

1.💡CDN (Content Delivery Network):

Caches content at edge locations worldwide, reducing latency by serving users from the nearest node.

➡️ Useful when: You're serving static assets globally or streaming media at scale.

2.💡Message Queue:

Decouples services by letting them communicate asynchronously through a queue (like RabbitMQ or Kafka).

➡️ Useful when: You need reliable communication between microservices or handling traffic spikes.

3.💡Rate Limiter:

Controls the number of requests a user or service can make within a time window.

➡️ Useful when: You're protecting APIs from abuse or managing resource allocation.

4.💡Circuit Breaker:

Prevents cascading failures by stopping requests to a failing service and allowing it time to recover.

➡️ Useful when: You're building resilient microservices that depend on external APIs.

5.💡Database Sharding:

Splits a database into smaller pieces (shards) distributed across multiple servers.

➡️ Useful when: Your database is too large for a single server or you need horizontal scaling.

6.💡Caching Layer:

Stores frequently accessed data in memory (Redis, Memcached) to reduce database load.

➡️ Useful when: You're optimizing read-heavy applications or reducing latency.

7.💡API Gateway:

A single entry point that handles routing, authentication, and rate limiting for all your microservices.

➡️ Useful when: You're managing multiple services and need centralized control.

8.💡Reverse Proxy:

Sits in front of servers and forwards client requests, adding security, caching, and load balancing.

➡️ Useful when: You need SSL termination, compression, or hiding server architecture.

9.💡Event-Driven Architecture:

Services react to events rather than direct calls, enabling loose coupling and real-time processing.

➡️ Useful when: Building real-time analytics, notifications, or audit logging systems.

10.💡CQRS:

Separates read and write operations into different models, optimizing each for its specific purpose.

➡️ Useful when: You have complex domains with different read/write performance needs.


What did I miss?

Solving system design problems is HARD.

1 mega-template to solve any problem:


Approaching system design problems can be harder than coding problems.

For one reason: ambiguity.

13 concepts to use when solving a system design problem:


0. Load Balancer:

A load balancer efficiently distributes incoming network traffic across multiple servers to ensure no single server bears too much load.

1. API Gateway:

An API Gateway acts as a single entry point for all client requests, providing a unified interface to various microservices.

2. Cache:

Caching stores recently accessed data for speedy retrieval, reducing the load on databases and improving response times.

3. CDN:

A Content Delivery Network (CDN) stores static content closer to users to minimize latency and speed up content delivery.

4. Distributed File Storage:

Distributed file storage ensures data is redundantly stored across multiple locations for fault tolerance and easy access.

5. Search Index:

Using a search index like Elasticsearch allows for scalable and fast search capabilities across large datasets.

6. Notification Service:

A notification service handles the delivery of messages or alerts to users across different platforms and devices.

7. Data Processing Systems:

Data processing systems like Hadoop and Spark analyze large volumes of data, enabling scalable and complex computational operations.

8. Distributed Scheduler: 

A distributed scheduler orchestrates and manages the execution of distributed tasks across various computing resources.

9. Metadata Server:

The metadata server stores information about the structure of data, enabling efficient access and management.

10. Block Server:

Block servers manage the storage blocks in distributed file systems, which helps in data replication and retrieval.

11. Distributed Logging:

Distributed logging collects logs from various services in a centralized manner for monitoring and troubleshooting.

12. Replication and Partitioning:

Replication ensures data availability and durability, while partitioning helps in scaling databases by distributing data across different nodes.


Reference (aff.):

🔥 Grokking the system design interview: 
https://lnkd.in/dcMM8sZM


What did I miss?


~~~

👉🏻 Join 50,001+ software engineers getting curated system design deep dives, trends, and tools (it's free):
➔ https://lnkd.in/dkJiiBnf

~~~

If you found this valuable:

👨🏼‍💻 Follow Alexandre Zajac
🔖 Bookmark this post for later
♻️ Repost to help someone in your network

11 Algorithms Every Engineer Should Still Know in 2026:


0.💡 Breadth-First Search (BFS): 

Explore a graph level by level, starting from the root, which is great for finding the shortest path in unweighted graphs. 

➡️ Useful when: You're designing web crawlers or analyzing social networks.


1.💡 Two Heaps: 

Uses a min-heap and max-heap to manage dynamic datasets efficiently, maintaining median and priority. 

➡️ Useful when: You need to manage a priority queue or dynamic datasets.


2.💡 Two Pointers: 

This technique takes 2 points in a sequence and performs logic based on the problem.

➡️ Useful when: You are implementing sorting or searching functions.


3.💡 Sliding Window: 

Optimizes the computation by reusing the state from the previous subset of data. 

➡️ Useful when: You're handling network congestion or data compression.


4.💡 Depth-First Search (DFS): 

Explores each path to the end, ideal for situations that involve exploring all options like in puzzles. 

➡️ Useful when: You're working with graph structures or need to generate permutations.


5.💡 Topological Sort: 

Helps in scheduling tasks based on their dependencies. 

➡️ Useful when: You are determining execution order in project management or compiling algorithms.


6.💡 Merge Intervals: 

Optimizes overlapping intervals to minimize the number of intervals. 

➡️ Useful when: Scheduling resources or managing calendars.


7.💡 Backtracking: 

It explores all potential solutions systematically and is perfect for solving puzzles and optimization problems. 

➡️ Useful when: Solving complex logical puzzles or optimizing resource allocations.


8.💡 Trie (Prefix Tree): 

A tree-like structure that manages dynamic sets of strings efficiently, often used for searching. 

➡️ Useful when: Implementing spell-checkers or autocomplete systems.


9.💡 Flood Fill: 

It fills a contiguous area for features like the 'paint bucket' tool. 

➡️ Useful when: Working in graphics editors or game development.


10.💡 Segment Tree: 

Efficiently manages intervals or segments and is useful for storing information about intervals and querying over them. 

➡️ Useful when: Dealing with database range queries or statistical calculations.


What did I miss?

You are what you eat.

10 brain foods to grow as an engineer:


0. Why Clock Synchronization Makes Engineers Lose Sleep:
► https://lnkd.in/gkDbWARS

1. How ChatGPT Actually Stays on Track (by Neo):
► https://lnkd.in/ggNMHDuV

2. How to Build Internal AI Agents That Actually Work:
► https://lnkd.in/gkJKyQnm

3. Scaling LLMs to Larger Codebases:
► https://lnkd.in/gWwfibJQ

4. Connection Pooling Finally Explained in 6 Minutes (by Nikki):
► https://lnkd.in/gkx6eNSG

5. 5 Engineering Dogmas It's Time to Retire (by Anton):
► https://lnkd.in/gYTdTmap

6. The System That Eliminates Interruptions for 10x Engineers (by Francisco):
► https://lnkd.in/g65_i6jf

7. Why Logging Sucks (And Everyone Knows It):
► https://loggingsucks.com/

8. Internal Dev Tooling at Meta (from Gergely):
► https://lnkd.in/g_A_BxFd

9. The Future of Software Development Is Software Developers:
► https://lnkd.in/gvzpK2CM


I am packaging the other deep dives and trends in today's issue of Hungry Minds.

Join 50,001+ software engineers from big tech to startups here: https://lnkd.in/dkJiiBnf


What are you reading this week?


#softwareengineering #systemdesign #programming

Full Stack developer | content creator| open for collabs| freelancer

🚀 Relentlessly Growing 1% Every Day

I’m a Senior Backend Developer currently working at 42Gears, where I focus on
building scalable, reliable, and high-performance backend systems used at
enterprise scale.

Previously, I’ve worked in a startup environment at AstrotimeApp, which
strengthened my ability to move fast, take ownership, and deliver
production-ready solutions end-to-end.

My journey in tech is driven by continuous learning and real-world problem
solving. I believe in improving a little every day—whether it’s mastering a
new framework, refining system design, or optimizing performance in
distributed systems.

💻 Core Technical Expertise

Backend & Systems  
• Java, Spring Boot  
• Node.js, Express.js  
• .NET  
• Golang, Python  

Databases & Messaging  
• MongoDB, Microsoft SQL Server  
• Redis  
• Apache Kafka  
• MQTT  

Frontend (Working Knowledge)  
• AngularJS, Angular  
• TypeScript  

Cloud & DevOps  
• AWS  
• Docker  
• Microservices Architecture  
• Nginx  

🌱 What I’m Focused On Right Now  
• Designing and scaling microservices  
• Improving system reliability, performance, and observability  
• Deepening expertise in cloud-native architectures  
• Writing clean, maintainable, production-grade backend code  

🤝 Open to sharing learnings, career guidance, and real-world backend
experience with developers and students.

👉 Book a 1:1 career guidance session here:  
https://topmate.io/kartik_kaushik11

Kartik Kaushik

Hi LinkedIn family,

I’m writing this post with a lot of hope.

My sister has recently moved to the UK for her higher studies.
Like many international students, she is trying to stand on her own feet
and manage her living expenses while pursuing her dreams.

As her brother sitting miles away this is one of those moments where
you feel helpless  wanting to do more but limited by distance.
So today I’m turning to this community.

She is urgently looking for job opportunities in areas like
sales, marketing, retail, or any suitable entry-level roles.
She is hardworking, sincere, and ready to learn.

If anyone here can help with leads, referrals, guidance,
or even a small direction it would mean more to us than I can express.

I can share her LinkedIn profile and resume with anyone willing to help.

Thank you for taking a moment to read this.
Grateful for the power of this community.

#UKJobs #StudentJobsUK #HiringUK #Referrals #InternationalStudents
#SalesJobs #MarketingJobs #LinkedInCommunity

Senior Software Engineer | content creator| open for collabs| freelancer | 1:1 Career Guidance

Hi Linkedin Community 

A Great List of Tech Articles to Read This Weekend:

1)How Uber built replication system to handle ~300,000,000 GB 
 👇 by Kamran Ali
https://lnkd.in/gMXJCnWu

2)Coding Challenge #105 - Top Programming Stories  by John Crickett:https://lnkd.in/g6eXKaPi

3)Sticky Sessions or Not? : By Saurabh Dashora
https://lnkd.in/gzmUk5zT

4)10 dead simple tips to start : 👇:. By Alexandre Zajac
https://lnkd.in/gYSAiyza

6)Vector Search vs Lexical Prefiltered , by Nikki Siapno
https://lnkd.in/gTGGhEaU

7)Coinbase scaled their hiring to 150 engineers per month over the pandemic.
? Gregor Ojstersek
https://lnkd.in/gFw4KMFu

8) Building AI agents is easy, getting them to survive production  By Mayank A.:
https://lnkd.in/gpSnXaAG

9)System Design Interview: Design Airbnb✨ By Neo Kim :https://lnkd.in/gqj3ufpF

10)The biggest lie in software architecture: By Raul Junco:https://lnkd.in/g9p8Sj6T

11)CAP Theorem Explained in simple terms:
: by Ashish Pratap Singh:https://lnkd.in/gH7u6NMH

12)Scaling monorepos does not mean rebuilding everything. . By Slobodan Gajić
https://lnkd.in/gEKxpurm

13)𝗦𝘂𝗯𝗮𝗿𝗿𝗮𝘆 𝘃𝘀 𝗦𝘂𝗯𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗲 𝘃𝘀 𝗦𝘂𝗯𝘀𝘁𝗿𝗶𝗻𝗴 𝘃𝘀 𝗦𝘂𝗯𝘀𝗲𝘁
: By NeetCode: https://lnkd.in/gJDPDBQQ

14)16 important DSA Problem Patterns you should look at while preparing for coding By Fernando Franco:https://lnkd.in/gkckTJww

15) Modern Storage Systems. By Alex Xu

https://lnkd.in/gbemmdDy

16)If you cannot build an AI agent without a library, you do not understand how it works.
 by Nina Fernanda Durán
https://lnkd.in/gGFcmvRr

17) Angular v21.1 is released and one of my favorite new features is support for the spread operator ​🧪 🚀 Roberto Heckers
https://lnkd.in/gsBWiBdD

18)In a Monolith, debugging is easy. By sukhad anand
https://lnkd.in/gCPUyb-T

19)"Can you read 900 words per minute?"
 By Addy Osmani: https://lnkd.in/gGPbQHb5

20)Career tip for 2026: Go all in on AI.. By Daniel Moka: 
https://lnkd.in/gp_D5svz

21)🎯I bombed my Google system design interview.
 Abhishek Kumar:https://lnkd.in/gHZpvABy

22)Trying to be cool is the fastest way to stay invisible.
:.By Taha Hussain:https://lnkd.in/g5TAA-J4

23)If a function accepts multiple, Petar Ivanov:https://lnkd.in/g6M5XWax

24) Here are 9 must read books if you have made a resolution to get serious about 
 By Mahesh Mallikarjunaiah ↗️
https://lnkd.in/gkgQXsAF

25)How to Build Your First ChatGPT App By Jordan Cutler:https://lnkd.in/gfZ_2c5R

26)Data Annotations are great for simple things. by Milan Jovanović https://lnkd.in/gYF4A9cH

27)CEO: "Don't be filtered. I want people to tell me what they really think." by Irina Stanescu
https://lnkd.in/gfEmc7y7

28)𝗦𝗹𝗶𝗰𝗲 𝗯𝘂𝗴𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗱𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀 𝗯𝘂𝗴𝘀 𝗶𝗻 𝗚𝗼 by Rihab SAKHRI:https://lnkd.in/gJr9NM4P

29)TCP is a single stream By Hussein Nasser :https://lnkd.in/gDT9kRiN

Hi LinkedIn community,
 🙏 Jai Shree Krishan to everyone!

📩 Message Brokers 

 Let’s break it down section by section 👇

1)📨 What is a Message Queue?
At its core, a message queue sits between producers and consumers.
Producer sends messages
Message Queue stores them temporarily
Consumer processes them asynchronously

📌 Key benefit: decoupling
Producers don’t wait for consumers
Consumers can be slow or down
System remains stable
This is the foundation of resilient architectures.

2)🔔 Publish–Subscribe Pattern (Event-Driven Systems)
In the right side of the image, notice the New Order Topic.
Here’s what’s happening:
Order Service publishes an OrderCreated event
Multiple services subscribe:
Inventory Service
Shipping Service
Notification Service
📌 One event → multiple independent reactions
 📌 No direct service-to-service calls
This is how event-driven microservices scale without becoming tightly coupled.

3)🔄 Steps in a Messaging System (End-to-End Flow)
The middle section shows the full lifecycle of a message:

1️⃣ Production
Producer creates and sends a message.

2️⃣ Ingestion
Broker:
Stores the message
Replicates it for fault tolerance

3️⃣ Consumption
Consumer fetches the message from the broker.

4️⃣ Processing
Business logic is executed:
DB writes
API calls
Side effects

5️⃣ Acknowledgment
Consumer confirms successful processing.
📌 Until acknowledgment → message is not considered done.
⚖️ Competing Consumers Pattern (Horizontal Scaling)

The bottom section shows multiple consumers competing for messages.
All consumers read from the same queue
Each message is processed by only one consumer
Broker load-balances messages

📌 This enables:
Horizontal scaling
Parallel processing
Faster throughput
Add more consumers → system scales automatically.

🧠 Why this matters in real systems
This entire flow gives you:
Fault tolerance
Loose coupling
Scalability
Back-pressure handling
And this is why message brokers are used in:
Order processing
Payments
Notifications
Data pipelines
Event sourcing

🧩 Final takeaway
This diagram is not about queues or topics.
 It’s about designing systems that don’t break under load.
If you can explain:
Message queues
Pub-sub
Message lifecycle
Competing consumers

Image credits : Alex Xu (ByteByteGo)

#MessageBrokers #SystemDesign #DistributedSystems #EventDrivenArchitecture #BackendEngineering #Kafka #RabbitMQ #Scalability

Hi LinkedIn Community,
 Jai Shree Krishna to everyone 🙏

📌 How Pinterest Built an Async Compute Platform for Billions of Task Executions

Every time you save a Pin on Pinterest, it looks instant.
 Behind the scenes, however, billions of background jobs are being executed every single day — image processing, notifications, analytics, recommendations, and more.

This is the story of how Pinterest evolved from Pinlater → Pacer, and why it’s a gold-standard system design case study.

1)🔁 Why Async Processing Is Critical
Not all tasks need to run synchronously.
Pinterest immediately acknowledges user actions while pushing heavy work into background queues. This keeps the UI fast — but at massive scale, async systems become extremely hard to operate correctly.

2)⚠️ Where Pinlater Broke at Scale
Pinlater worked well initially, but growth exposed fundamental flaws:
🔸 Database lock contention
 Every queue was spread across all DB shards, forcing multiple servers to scan the same partitions concurrently.
🔸 No workload isolation
 CPU-heavy image processing jobs ran alongside lightweight notification jobs — one failure could take everything down.
🔸 Shared critical & non-critical paths
 Enqueue (user-facing) and dequeue (background) operations competed for the same resources.
🔸 Massive inefficiency
 Over 50% of DB queries returned empty results.
🔸 No FIFO guarantees
 Jobs were processed from multiple shards with no global ordering.
Scaling infrastructure only made things worse.

3)🚀 Pacer: A Ground-Up Redesign
Instead of incremental fixes, Pinterest rebuilt the system.

🧩 Key Architectural Innovations
🔹 Dedicated Dequeue Broker (Stateful Layer)
 Each queue partition is owned by exactly one broker, coordinated using Helix + ZooKeeper.
 👉 Lock contention disappeared completely.
🔹 In-Memory Job Buffers
 Brokers prefetch jobs and serve workers directly from memory.
 👉 Dequeue latency dropped to sub-millisecond (<1ms).
🔹 Adaptive Sharding
Small queues → few partitions
Large queues → many partitions
 👉 Empty DB scans dropped from 50% → nearly zero.

🔹 FIFO Ordering (Finally!)
 Single-partition queues now guarantee strict ordering when required.
🔹 Isolated Execution via Kubernetes
 Each queue runs in its own pod with custom CPU/memory/concurrency.
 Failures are contained. Performance tuning is finally possible.
🔹 Critical Path Isolation
Enqueue = user-facing & always reliable
Dequeue = backend processing
 Even if workers slow down, users never notice.

4)📈 Measurable Impact
✅ Lock contention eliminated
 ✅ Linear scalability by adding brokers
 ✅ Faster job execution
 ✅ Better hardware efficiency
 ✅ Zero cross-queue blast radius


#SystemDesign #DistributedSystems #Scalability #BackendEngineering #AsyncProcessing #Pinterest #TechArchitecture #EngineeringLeadership

Hi LinkedIn community 👋
 Jai Shree Krishna to everyone 🙏✨

Netflix is no longer just a streaming platform 🎬

 It now spans live events, mobile gaming 🎮, ads, and hyper-personalized experiences—and that evolution created one of the most fascinating real-time distributed systems challenges at internet scale.

1)🎯 The Core Problem
A single Netflix member might:
Watch Stranger Things on a smartphone 📱
Continue on a smart TV 📺
Launch the Stranger Things mobile game on a tablet 🎮
Different devices. Different services. Different times.
 Yet it’s all one member journey 🧩
Behind the scenes, Netflix runs hundreds of microservices, each owning its own data store.
 Streaming data, gaming data, authentication data—all lived in silos. Traditional data warehouses couldn’t connect these relationships in real time.

2)🧠 Why Netflix Chose a Graph
Because member journeys are relationships, not rows.
Graphs enabled Netflix to:
⚡ Traverse relationships without expensive joins
🔄 Adapt easily as new connections appeared
🔍 Detect patterns across devices, content, and behavior
This led to RDG — Real-Time Distributed Graph.

3)🏗️ How RDG Works (Simplified)
Kafka 📩 ingests millions of real-time events per second
Flink 🔄 processes, enriches, deduplicates, and converts events into nodes & edges
KVDAL (built on Cassandra) 🗄️ stores the graph using adjacency lists for ultra-fast traversal
Instead of relying on traditional graph databases (which struggled with scale and ingestion), Netflix built graph behavior on top of proven distributed systems.

4)🧩 Smart Design Decisions
One Kafka topic → one Flink job (simpler tuning & reliability)
Separate namespaces for each node and edge type
Adjacency lists for lightning-fast reads ⚡
Fine-grained TTLs to control graph growth
Simple ideas. Massive impact.

5)📈 The Scale (Production Numbers)
🟢 8+ billion nodes
🔵 150+ billion edges
⚡ ~2M reads/sec
✍️ ~6M writes/sec
⏱️ Single-digit millisecond latency
And the system scales linearly 🚀
🔑 The Big Takeaway
Netflix didn’t chase shiny technology.
 They optimized for reliability, operational simplicity, and real-time performance.
This is engineering pragmatism at internet scale 🧠🔥
If you’re building systems involving personalization, real-time context, or cross-platform user experiences, Netflix’s RDG is a blueprint worth studying.

#SystemDesign #DistributedSystems #NetflixEngineering #GraphArchitecture #BackendEngineering #DataEngineering #RealTimeSystems #Microservices 🚀

Hi LinkedIn community,
Jai Shree Krishna to everyone 🙏

🚀 We’re Hiring at 42Gears | Multiple Open Positions – Bangalore 🚀
At 42Gears, we build secure, scalable, and enterprise-grade Unified Endpoint Management (UEM) & Device Management solutions trusted by organizations worldwide.
We are expanding our teams across Engineering, Cloud, Security, QA, DevOps, UI, Sales, and Design.
If you’re passionate about technology, innovation, and solving real-world enterprise problems, we’d love to connect with you!

🌟 Why Join 42Gears?
✔ Work on global enterprise products used by customers worldwide
 ✔ Exposure to cutting-edge technologies in Cloud, Security, MDM & UEM
 ✔ Strong learning culture with mentorship from experienced leaders
 ✔ Opportunity to own impactful features end-to-end
 ✔ Career growth in a fast-scaling, product-based organization
 ✔ Collaborative, transparent, and high-performance work culture

🔹 Current Openings

A)👨‍💻 Engineering & Product Development

1)Senior Software Engineer (Windows / Device Platform) – 3–4 yrs
Skills: C#, .NET, WPF/XAML, WinForms, MVVM, Windows Internals

2)Senior Software Engineer (macOS / Apple Platform) – 3+ yrs
Skills: Swift, Objective-C, AppKit, LaunchAgents, XPC, macOS Security Frameworks

3)Software Developer (Java & Linux) – 0–5 yrs
Skills: Core Java, OOP, Design Patterns, JVM, Linux, Debugging

4)Software Engineer – Freshers (Java) – 0 yrs
Skills: Java, OOP, Design Patterns, Test Automation, Problem Solving

5)Senior UI Developer – 4–5+ yrs
Skills: Angular (v10+), TypeScript, RxJS, HTML5, CSS3, SCSS, REST APIs

B)☁️ Cloud & Infrastructure
a)Lead Cloud Engineer – 6–8 yrs
Skills: AWS, Azure, GCP, VPC/VNet, IAM, Security, Observability, DR, Cost Optimization

b)Associate Cloud Manager – 10+ yrs
Skills: Cloud Strategy, Governance, Hybrid Cloud, Team Leadership, Agile Delivery

c)🔧 DevOps & QA
DevOps Engineer / Jr. DevOps Engineer – 0–3 yrs
Skills: CI/CD (Jenkins/GitHub Actions), Docker, Kubernetes, AWS/GCP, Linux

d)Test Engineer (QA) – 1–3 yrs
Skills: SSO, IDP (Okta, Google, Microsoft Entra), OAuth, SAML, MDM Testing
🔐 Information Security

e)Senior Security Engineer I – 5+ yrs
Skills: VAPT, Thick-Client Security, Source Code Review
Senior Security Analyst – Malware & Application Security

f)🎨 Design
a)Senior Graphic Designer – 3–5 yrs
Skills: Adobe Photoshop, Illustrator, InDesign, Figma, Video Editing
💼 Sales
b)Channel Sales Manager – 8–10 yrs
Skills: Partner Management, SaaS Sales, Channel Enablement

c)Sales Executive – 0–2 yrs
Skills: Field Sales, Lead Generation, Partner Ecosystem

d)🎓 Internship
Intern – Professional Services – Freshers
Skills: SQL, SDLC, Customer Support, Product Deployment

📍 Location: Bangalore
🕘 Employment Type: Full-Time (Internship available)
📧 How to Apply:
👉 Please share your resume at: kartikgraphic6@gmail.com

📌 Mention  Role in the subject line (– Lead Cloud Engineer)


#Hiring #42Gears #BangaloreJobs #TechHiring #CloudJobs

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Alexandre Zajac

Kartik Kaushik

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The Battle

Alexandre Zajac

Kartik Kaushik

Followers

Posts

Engagements

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Alexandre Zajac's LinkedIn Performance

Performance ScoreKartik Kaushik's LinkedIn Performance

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Performance ScoreAlexandre Zajac's LinkedIn Performance