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ClanX cover picture
Founded :
2023
Type :
Products & Services
Size :
0-20
Stage :
Bootstrapped

About

ClanX brings world-class product builders together as cloud-based teams and connects them with companies that have meaningful product missions.


Our vision is to transform the way companies and talent collaborate, creating a harmonious ecosystem where both thrive, inspire, and achieve greatness together.


Innovation and simplicity are at the core of our DNA. Through our revolutionary AI-powered platform, we seamlessly connect exceptional talent with visionary companies, crafting the perfect match.


Our transformative platform orchestrates partnerships that inspire greatness, bringing together the world’s best minds and companies to shape a future of limitless possibilities in the tech world.

Read more

Tech stack

skill iconPython
skill iconMachine Learning (ML)
Generative AI
Natural Language Processing (NLP)
OCR

Candid answers by the company

What is the location preference of jobs?
What does ClanX do?

The job location is Bangalore, hybrid

Company social profiles

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Jobs at ClanX

ClanX
at ClanX
2 candid answers
Ariba Khan
Posted by Ariba Khan
Mumbai
4 - 10 yrs
Upto ₹35L / yr (Varies
)
skill icon.NET
skill iconReact.js
skill iconC#
TypeScript

This opportunity through ClanX is for Hudl (direct payroll with Hudl)


Please note you would be interviewed for this role at Hudl while ClanX is a recruitment partner helping Hudl find the right candidates and manage the hiring process.


About the company:

Hudl is a global sports technology company helping teams and athletes capture video, analyze performance, and share insights. Hudl supports sports at all levels through products like Hudl Assist, Wyscout, Instat, and Volleymetrics.


Requirements:

  • 4+ years of commercial experience in full-stack development
  • Experience across frontend, backend, and database layers
  • Exposure to C# and TypeScript is a strong plus
  • Cloud experience, preferably AWS
  • Strong English communication skills
  • Comfortable working on ambiguous problem statements
  • Experience collaborating with remote, cross-cultural teams
  • Currently working at a product based organsiation


Responsibilities:

  • Build and enhance full-stack web features across multiple Hudl products
  • Develop and optimize internal tools and workflows for sports analysts
  • Improve queue systems and operational efficiency for game analysis
  • Collaborate with Engineering, QA, Product, Design, and Scrum teams
  • Propose technical solutions and support junior engineers
  • Contribute to rapid iteration and frequent production deployments


Interview Process

  • HR Screening
  • Online Assignment
  • Panel Technical Round
  • Final Round – Technical and Culture Fit
Read more
ClanX
at ClanX
2 candid answers
Ariba Khan
Posted by Ariba Khan
Bengaluru (Bangalore)
5 - 8 yrs
Upto ₹55L / yr (Varies
)
skill iconPython
Natural Language Processing (NLP)
skill iconMachine Learning (ML)
OCR
Large Language Models (LLM)
+1 more

This opportunity through ClanX is for Parspec (direct payroll with Parspec)


Please note you would be interviewed for this role at Parspec while ClanX is a recruitment partner helping Parspec find the right candidates and manage the hiring process.


Parspec is hiring a Machine Learning Engineer to build state-of-the-art AI systems, focusing on NLP, LLMs, and computer vision in a high-growth, product-led environment.


Company Details:

Parspec is a fast-growing technology startup revolutionizing the construction supply chain. By digitizing and automating critical workflows, Parspec empowers sales teams with intelligent tools to drive efficiency and close more deals. With a mission to bring a data-driven future to the construction industry, Parspec blends deep domain knowledge with AI expertise.


Requirements:

  • Bachelor’s or Master’s degree in Science or Engineering.
  • 5-7 years of experience in ML and data science.
  • Recent hands-on work with LLMs, including fine-tuning, RAG, agent flows, and integrations.
  • Strong understanding of foundational models and transformers.
  • Solid grasp of ML and DL fundamentals, with experience in CV and NLP.
  • Recent experience working with large datasets.
  • Python experience with ML libraries like numpy, pandas, sklearn, matplotlib, nltk and others.
  • Experience with frameworks like Hugging Face, Spacy, BERT, TensorFlow, PyTorch, OpenRouter or Modal.


Requirements:

Must haves

  • Design, develop, and deploy NLP, CV, and recommendation systems
  • Train and implement deep learning models
  • Research and explore novel ML architectures
  • Build and maintain end-to-end ML pipelines
  • Collaborate across product, design, and engineering teams
  • Work closely with business stakeholders to shape product features
  • Ensure high scalability and performance of AI solutions
  • Uphold best practices in engineering and contribute to a culture of excellence
  • Actively participate in R&D and innovation within the team

Good to haves

  • Experience building scalable AI pipelines for extracting structured data from unstructured sources.
  • Experience with cloud platforms, containerization, and managed AI services.
  • Knowledge of MLOps practices, CI/CD, monitoring, and governance.
  • Experience with AWS or Django.
  • Familiarity with databases and web application architecture.
  • Experience with OCR or PDF tools.


Responsibilities:

  • Design, develop, and deploy NLP, CV, and recommendation systems
  • Train and implement deep learning models
  • Research and explore novel ML architectures
  • Build and maintain end-to-end ML pipelines
  • Collaborate across product, design, and engineering teams
  • Work closely with business stakeholders to shape product features
  • Ensure high scalability and performance of AI solutions
  • Uphold best practices in engineering and contribute to a culture of excellence
  • Actively participate in R&D and innovation within the team


Interview Process

  1. Technical interview (coding, ML concepts, project walkthrough)
  2. System design and architecture round
  3. Culture fit and leadership interaction
  4. Final offer discussion
Read more
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