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YO IT Consulting Tech Stack

AI model evaluation and training data services for LLM and coding applications

Software Development Abu Dhabi, Abu Dhabi Emirate 51–200 employees Founded 2018 Privately Held

YO IT Consulting operates as a specialized AI services firm focused on model evaluation, training data generation, and quality assurance for large language models—particularly in code reasoning and engineering contexts. The hiring mix is heavily weighted toward data (243 roles) and research (170 roles) relative to engineering (398), suggesting a labor-intensive evaluation and annotation operation rather than a software-product company. Current projects center on LLM code response evaluation, dataset creation for fine-tuning, and model benchmarking, while pain points cluster around accuracy, reliability, and code-generation quality—all upstream-of-deployment problems typical of data-quality service providers.

Tech Stack 154 technologies

Core StackPython PyTorch TensorFlow Weights & Biases MLflow Docker Kubernetes Apache Airflow Intune AWS Zscaler Oracle Power BI React AWS Lambda CloudWatch Terraform Azure DevOps Azure AD Nginx JAX Windows Microsoft 365 Azure Entra ID GCP SharePoint API Gateway AWS Fargate AWS Application Load Balancer Aurora+124 more
AdoptingPOS ERP CRM

What YO IT Consulting Is Building

Challenges

  • Improving ai model accuracy
  • Improving ai coding output quality
  • Improving ai system reliability
  • Ensuring ai-generated code quality
  • Reducing errors in ai-generated code
  • Evaluating ai-generated code accuracy
  • Llm content accuracy
  • Ensuring ai response accuracy
  • Improving model performance
  • Improving mathematical reasoning in ai

Active Projects

  • Ai code reasoning evaluation
  • Data annotation project
  • Llm response evaluation for coding queries
  • Benchmarking model responses
  • Model reasoning evaluation for engineering concepts
  • Ai training content development
  • Research project for top ai company
  • Assess ai-generated solutions across model training, debugging, optimization, experimentation
  • Dataset creation for supervised fine-tuning
  • Evaluating ai-generated code responses

Hiring Activity

Accelerating1,200 roles · 1,200 in 30d

Department

Engineering
398
Data
243
Research
170
Design
144
Finance
39
Security
34
Consulting
30
Marketing
21

Seniority

Senior
484
Mid
466
Junior
163
Director
21
Manager
15
Lead
4

Notable leadership hires: Plant Head, Creative Director

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About YO IT Consulting

YO IT Consulting is a privately held AI services company headquartered in Abu Dhabi, founded in 2018, with 51–200 employees. The firm specializes in developing and deploying AI solutions—including machine learning, data analytics, automation, NLP, and computer vision—across industries. The company's active project portfolio focuses on AI model evaluation, data annotation, training dataset creation for supervised fine-tuning, and benchmarking model responses, particularly for coding and engineering applications. Its technology foundation spans Python, PyTorch, TensorFlow, and JAX for model work; Kubernetes and AWS infrastructure for deployment; and Power BI for analytics. The firm is currently in rapid hiring expansion, with 1,208 open roles posted in the last 30 days across engineering, data, research, and design functions, and is recruiting globally across 25+ countries including the US, India, Thailand, Belgium, and the UK.

HeadquartersAbu Dhabi, Abu Dhabi Emirate
Company Size51–200 employees
Founded2018
Hiring MarketsUnited States, India, Thailand, Belgium, Uganda, Vietnam, Monaco, Nigeria

Frequently Asked Questions

What is YO IT Consulting's tech stack?

Core ML frameworks include Python, PyTorch, TensorFlow, and JAX. Infrastructure runs on Docker, Kubernetes, AWS (Lambda, Fargate, Aurora), and GCP. ML ops uses MLflow and Weights & Biases; orchestration via Apache Airflow and Terraform. Microsoft stack includes Azure DevOps, Intune, and Microsoft 365.

What is YO IT Consulting working on?

Primary projects focus on AI model evaluation, data annotation, LLM response evaluation for coding queries, dataset creation for supervised fine-tuning, and benchmarking model reasoning for engineering concepts. Pain points center on improving AI code generation quality, model accuracy, and system reliability.

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