Full stack developer | ML developer | Python , JavaScript , Typescript, Java , c++ , C# | React , Vue , Angular , React Native , Next.js , Babylon.js | Node.js , Django , Nestjs, Spring , Fastapi | Pandas , TensorFlow

With 15+ years in software engineering and deep learning, I bridge the gap between "proof of concept" and scalable, production-grade AI. I don’t just train models; I build the infrastructure that makes them reliable, secure, and profitable.Currently focused on architecting Cloud-Native Generative AI systems. I help companies move beyond basic prompting to build robust RAG pipelines and Agentic Workflows that actually work at scale.Where I deliver value:Production AI: Architecting end-to-end pipelines using Kubernetes (EKS/GKE) and Serverless infrastructure to serve models with &lt;100ms latency.Advanced LLM Ops: Fine-tuning Transformers (PyTorch/JAX) and implementing Vector Search (RAG) for context-aware applications.Measurable Impact: Proven track record of improving forecast accuracy by 25% and accelerating deployment cycles by 30% through rigorous CI/CD for ML.Technical Arsenal:Core AI: PyTorch, TensorFlow, Hugging Face, LangChain.Infra: Docker, Kubernetes, Terraform, AWS/GCP/Azure.Data: Apache Spark, Airflow, Vector Databases.I am passionate about Responsible AI—ensuring models are explainable, privacy-compliant (GDPR), and secure.🎥 See how I build: https://www.youtube.com/@rez-dev

3 ways I stopped using AI at work:
Final decisions → AI can miss context
Sensitive content → not worth the risk
“Make it sound professional” → can become bland “botspeak”
Instead, I use it for:
summarising
first drafts
brainstorming
structure + clarity checks
repeatable automation
Where has AI not been worth it for you?
#ArtificialIntelligence #WorkSmart #ProfessionalDevelopment

Reza Zeraat

As a highly motivated and experienced Full Stack Developer, I am passionate about building functional and visually appealing web applications. With 7 years of experience working with React and JavaScript, 5 years of experience with back end technologies, and expertise in Node.js and Python Django, I have successfully built web applications and REST APIs. I am well-versed in both SQL and No-SQL databases such as PostgreSQL, MySQL, MongoDB, and Redis.

In addition to my technical skills, I have 2 years of experience in DevOps using Amazon Web Services (AWS) and Digital Ocean. As a project manager, I have implemented Agile methodologies, including Scrum and LEAN, to improve software development efficiency. With 2 years of experience as a mobile developer using React Native, I am equipped to take on any challenge in the Full Stack Development space.

If you're looking for a talented Full Stack Developer to collaborate with on amazing projects, don't hesitate to reach out to me on LinkedIn. I am a hard-working and ambitious individual, always seeking new adventures and challenges in the world of Application Programming.

Senior AI Engineer | Python Javascript Typescript Java C++ | React Vue Angular React Native Next.js | Node.js Django Nestjs SpringBoot Fastapi | Pandas TensorFlow LangChain Ray Transformers LLMs RAG MLOps

With AWS lambda you can create an api without the need of maintaining or devops issues.
This service is a gold in the desert can help to have an mvp which scaleable in AI industry.
The AI workload can easiliy handled without any issues and startups can get to product with low costs!
#AWS #Lambda #startup

AI is quickly becoming a daily work tool — not a “future thing”.
In my day-to-day, I’m seeing the biggest impact in:
Turning rough notes into clear emails, briefs, and updates
Summarising long docs and extracting key actions
Drafting first versions of plans, slides, and FAQs
Speeding up analysis (spreadsheets, trends, quick calculations)
Brainstorming alternatives when I’m stuck
The real shift isn’t “AI replacing people” — it’s AI reducing the boring parts so people can focus on judgment, creativity, and relationships.
A good rule I try to follow: use AI for drafts and options, not final decisions.
 And always sanity-check facts, numbers, and tone before hitting send.
Curious: what’s one AI use case that has genuinely saved you time each week?
#AI #Productivity #FutureOfWork #Automation #KnowledgeWork

If you still think “AI chips” means just GPUs, Cerebras is worth a look.
Their wafer-scale engine flips the usual approach: instead of stitching together lots of smaller chips, they run models on a single wafer-scale processor — and they’re claiming 3,000+ tokens/second output speeds for real-time inference. ⚡️
Why that matters:
Lower latency → snappier assistants and agents
Less bandwidth bottlenecking → faster end-to-end responses
A different scaling path beyond “more GPUs”
Curious where this goes next: do we end up with a mixed future of GPUs and specialised wafer-scale systems?
#AI #GenAI #Inference #Semiconductors #Cerebras #LLM

Security isn’t a tool. It’s a workflow.
What made the biggest difference for us:
threat modelling as part of design (not after launch)
secrets management + rotation (no “temporary” credentials)
least privilege by default
dependency scanning + patch cadence
security reviews focused on abuse cases, not checkboxes
“Secure by default” is cheaper than “secure later.”
What’s one security practice you wish teams adopted earlier?
#ApplicationSecurity #DevSecOps #ThreatModeling #SecurityEngineering #RiskManagement

Open Sourceress @ LlamaIndex

New Rust crate: 𝗮𝗿𝘅𝗶𝘃-𝗰𝗹𝗶🦀
I built a lightweight CLI app that allows you to download the most recent papers belonging to a specific category on arXiv, with their metadata, plain-text summaries and PDF versions📁
You can download metadata and text summaries for 1000 papers in 0.5s!🚀 
Install with 𝘯𝘱𝘮 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 @𝘤𝘭𝘦-𝘥𝘰𝘦𝘴-𝘵𝘩𝘪𝘯𝘨𝘴/𝘢𝘳𝘹𝘪𝘷-𝘤𝘭𝘪 or 𝘤𝘢𝘳𝘨𝘰 𝘪𝘯𝘴𝘵𝘢𝘭𝘭 𝘢𝘳𝘹𝘪𝘷-𝘤𝘭𝘪, and don't forget to take a look at the GitHub repo 👉  https://lnkd.in/e64vxbyT

Absolutely correct writing is the trigger of our brain to start

Building a Safer Internet with AI 🤖 | Scouting for top startups to invest in 💸 | The only newsletter you need for Finance & Tech at 🔔linas.substack.com🔔 | Financial Technology | FinTech | Artificial Intelligence | VC

Nailed it! Writing is thinking.

Don't let AI do it all.

If you outsource writing to AI, you’re not just outsourcing “the words.”

You’re outsourcing the thinking that creates the words.

The act of writing forces structure.

It forces decisions.
It forces tradeoffs.

Writing is where vague ideas either:

→ Become clear
or
→ Collapse under their own nonsense.

That friction is the whole point.

P.S. check out 🔔linas.substack.com🔔, it's the only newsletter you need for all things when Finance meets Technology. For founders, builders, and leaders.

Does anyone know any redis provider with generous free tier

2026 will be the rise of Hybrid Engineers 
The Developer that understands the systems and ai models together
Imagine we have a farm that sometimes need protection or feeding water in the past the sensors will give some signals about it then the human will check and proceed some actions but this year the system will handle the operations and agents which have access to sensor will manage everything 
Only creating a chatbot is easy now we need to have workflows that have fail tolerence and capable of executions.
Iot will be next stage of AI in 2026 and engineers who knows about it will be able to do some great things for companies around the world
#IOT #AgenticAI #AgenticWorkflow #AI #OpenAI

Sam Altman Says OpenAI “Screwed Up” GPT-5.2 Writing Quality
🤣🤣🤣🤣🤣
OpenAI Has lost the market
I think Sam is high 😁
#AI #memes

Ai is not about inference only it's about architecture too
The providers should think about all aspects in general
#AI #AgenticAI

Move beyond DAGs. Embrace the Cycle.
​In traditional LLM orchestration (like standard RAG), we build Directed Acyclic Graphs (DAGs). The execution flow is linear and rigid: Retrieve \rightarrow Augment \rightarrow Generate.
​To build true Agentic Workflows, you need to introduce Cycles.
​By allowing the graph to loop, you enable the Reasoning and Action trace to persist state across steps.
​The Loop: LLM generates a tool call \rightarrow Executor runs tool \rightarrow Output is fed back to LLM as observation.
​The Guardrail: Implement a Reflection node that critiques the output before finalizing the state.
​If your architecture can't handle a GOTO line back to the start based on a conditional check, you aren't building an agent; you're just building a fancy script.
​Are you using LangGraph, AutoGen, or rolling your own state machine?
​#LLMOps #AgenticAI #LangChain #SystemArchitecture #Python

If your app still requires users to manually type data from IDs or receipts, it's time for an upgrade.

​We’ve all been there: staring at a PDF invoice, manually copy-pasting numbers into Excel or a database. It’s slow, error-prone, and honestly, boring.

​AWS Textract changes the game by turning documents into actionable data instantly.

​Real-World Use Cases:

​FinTech: Instantly extract data from W-2s and Bank Statements to speed up loan approvals from days to minutes.

​Healthcare: Digitize patient intake forms (even handwritten ones!) to reduce administrative overhead.
​Expense Management: Snap a picture of a crumpled lunch receipt and automatically populate date, vendor, and total cost fields.

​Stop treating documents like images. Start treating them as data sources.

​#AWS #Textract #FinTech #DigitalTransformation #AI #Automation

Stop storing raw face images. Start using Vector Embeddings with AWS Rekognition. 🔐

​Building a facial verification system from scratch using OpenCV or dlib is a great learning exercise, but in production, you hit scalability and privacy walls fast.

​I’ve been digging into AWS Rekognition’s Face APIs, and the architecture for handling "Face Extraction" is impressive for two specific reasons:

​1. The IndexFaces Operation
Instead of storing user photos in your database (a GDPR nightmare), Rekognition detects the face, extracts the feature vector (embedding), and indexes it into a server-side "Collection."
​Input: Image bytes (S3 object or Blob).
​Output: FaceId (a unique UUID).
​Storage: AWS stores the vector, you just store the UUID.

​2. Anti-Spoofing with CreateFaceLivenessSession
Simple face comparison is easy to fool with a printed photo. Rekognition now handles the heavy lifting of Presentation Attack Detection (PAD) via their SDK, filtering out spoof attacks before they even reach your backend logic.
​For any AI engineers building auth flows: delegate the computer vision complexity so you can focus on the application logic.

​#AWS #Rekognition #ComputerVision #FaceID #CloudSecurity #DevOps #Python

If you wanna deploy your api with zero complexity and easy to maintenance use app runner cheap and simple good enough to handle low to medium workload enough for startups to go live!
#aws

The way that Ai is proceeding it is going to be a very tough time to get a job

If something is not working then it's time for a pivot!

If you really think about scrum and all those nonsense in swe dev is just a layer added to make this job more complex!
#scrum #jira #developer

I believe that companies nowadays must focus on the engineers which have ideas to make things great again
#developer

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