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OKF: The Wiki Your AI Agent Can Actually Read
Your agent is smart. Your company knowledge is scattered. Here is the small, plain-text standard that tries to fix the gap.The brilliant intern with no mapImagine hiring the sharpest intern you have ever met. Day one, you ask: "What counts as an active cu
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Deploy Claude on AWS: Three Proven Approaches
Amazon Bedrock Claude Platform on AWS AWS Marketplace Why Choose AWS to Deploy Claude?Most companies already have dozens of services running on AWS. Adding Claude to that stack - rather than setting up a completely separate vendor relationship - eliminate
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The Modern Data Governance Stack Why It Is Now the Prerequisite for an AI-Ready Platform
TL;DR Governance is now a prerequisite for AI readiness, enabling AI to discover, understand, trust, and safely use enterprise data. Modern governance must serve both business users and AI agents through catalogs, glossaries, lineage, DQ certification, ma
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Prompt Engineering vs. Prompt Injection: Building Smarter and Safer AI Applications
"The quality of an AI's response depends on the prompts you give it. AI safety comes from ensuring that the model follows trusted instructions and ignores malicious ones." Tools like Git-hub Copilot, Gemini, Claude and others AI systems are changing how w
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Monorepos: How to Build and Scale Multiple Related Apps
How It Usually Starts Let's say a team is working on a project. Everybody knows where everything is, mostly because there's only "one" of everything. Then the product takes off, somebody spins up a second app (an admin dashboard, usually), and it gets its
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Image Compression in Software Development
Picture this. For weeks you have been working on it. It runs beautifully over your Wi-Fi, on your machine, and on your top-of-the-line phone. You bring it. And then somebody opens it on a mid-range device in an area with slow-connectivity and it crawls. I
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Scaling Application performance with Hot, Warm, Cold data tiers
While working with long-term enterprise projects, everyone's focus is on implementing features, maintaining quality, and delivery execution. But gradually, as applications start growing, the problem shifts from features to handling large datasets. It is i
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From Prompt to Query: How We Built Natural Language Search Over Our Own Database
This started with a fairly simple customer request. They wanted to ask questions in plain English and get answers from data that already existed in our application database. We decided to build the feature using Amazon Bedrock and a Amazon Bedrock Knowled
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Why Model Context Protocol (MCP) Is Emerging as a Standard for Enterprise AI Integration
Let's address the massive, glowing red problem sitting right in the middle of the enterprise tech landscape.Enterprises are rushing to adopt AI assistants and automated agents. Leadership wants efficiency. Product teams want speed. Everyone is convinced t
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Why Your Enterprise LLM Costs Keep Climbing
A few months after an enterprise AI chatbot went live, the finance department called an emergency meeting. Nothing was broken. Users loved the platform. Adoption numbers looked fantastic. Leadership was celebrating. The AI assistant was answering customer
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Tech stack
Technologies named in job postings at Anblicks over the last 12 months, by layer, with the share of postings for each.
Technologies in use
31
Postings analysed
2
Stack modernity
85/100AI adopter
Languages
Python
Bash
Go
Node JS
JavaScript
SQL
Databases
Snowflake
Cloud & platforms
AWS
GCP
Kubernetes
Linux
Windows
Anthropic
Azure
OpenAI
Developer tools
Git
Rest API
Model Context Protocol
AI & ML
LLM
Claude
Practices & domains
Agentic Workflows
CI/CD
Function Calling
LLM Evaluation
Prompt Engineering
RAG
Structured Outputs
Time Series Forecasting
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Growth
Hiring Momentum
58/100
Growing
Open positions
2
2 opened / 0 closed in 30 days
ATS activity
Every ~3 hours
Today
Hiring Dynamics
0%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
AI/ML
50% ▲
Backend
50% ▲
Activity Timeline
New location: Hyderabad
Oct 2026
Offices
Where the company hires and what each office is for
Cities
3
Hiring now
2
Countries
2
Busiest office
Ahmedabad
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Headquarters
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Office with open vacancies
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Ahmedabad
India
Regional office
Ahmedabad
1 open roles 1 posted in 90 days 100% engineering
hires for AI/ML
Block-A, 801 to 806, 8th Floor, Navratna Corporate Park,Opp Jayantilal Park, Ambli Bopal Road, Ahmedabad 380058
Hyderabad
India
1 open roles 1 posted in 90 days 100% engineering
hires for Backend
Dallas
United States
Regional office
USA
14911, Quorum Drive, Suite 390, , Dallas, TX 75254, USA
Jobs
≈ $15k – $38k per year (Estimated) • In office • Full-Time • 6+ years exp • Hyderabad
Python
Go
JavaScript
Node JS
Bash
Snowflake
AI/ML
Time Series Forecasting
DevOps
GCP
Git
AWS
Kubernetes
Linux
Windows
Management
ServiceNow
Outlook
Apply
≈ $18k – $43k per year (Estimated) • In office • Full-Time • 3+ years exp • Ahmedabad
Python
SQL
Claude
Model Context Protocol
Prompt Engineering
Function Calling
LLM
RAG
OpenAI
Anthropic
LLMOps
Structured Outputs
Knowledge Graph
LLM Evaluation
Agentic Workflows
Tool Use
DevOps
Rest API
Azure
CI/CD
Git
Cybersecurity
Least Privilege
Management
Agile
Apply
