Jr ML Engineer (exp. 3y+, onsite-Gurgaon, full-time)

Experience: 3–4 years  

Location: On-site — Gurgaon, India  

Employment Type: Full-time

About tracebloc

tracebloc is a Berlin-based AI startup building tooling for data scientist, allowing them to evaluate and benchmark third-party AI models without the need to expose their data. We have recently received $2,5m in funding and are aiming to build the category leader in AI model discovery. 

About the Role

Junior ML Engineer

You will be independently responsible for architecting, developing, and maintaining a scalable ML platform — not just individual models — enabling robust and reusable workflows. 

on-site role based in Gurgaon

How to apply:

To help us better understand your hands-on capabilities, please include the following in your application: 

• A

  link to your Git repository

  (e.g., GitHub, GitLab) showcasing ML pipelines or deployment code you've worked on. 

  If you don’t have a public repo

  , you can share

  sample code

  demonstrating your skills,

  or

   submit a

  detailed project write-up

  describing the architecture, workflow, and your contributions. 
• A

  short Loom video (around 3 minutes)

  or screen recording

  explaining a project you’ve worked on

  . Walk us through your codebase or workflow, explaining: 

The structure of the solution 

Design decisions and trade-offs 

Technologies used 

,

Key Responsibilities

• Build and manage end-to-end ML pipelines (data prep, training, deployment, monitoring) 
• Deploy ML systems on cloud (AWS/Azure) using Docker/Kubernetes 
• Create reusable components to support multiple ML workflows 
• Write clean, testable Python code for production 
• Implement CI/CD for ML workflows. 
• Monitor and improve deployed models 

Required Skills

• Strong hands-on experience in Python, with proficiency in ML libraries such as

  scikit-learn, pandas, NumPy, PyTorch, tensorflow.

   
• Experience in building end-to-end

  ML pipelines

  (not notebooks or isolated scripts).  
• Deep understanding of pipeline design patterns and best practices for production environments.  
• At least one year experience in building ML Pipelines for

  Computer Vision

  and

  NLP tasks

  .  
• Good understanding of production best practices: versioning, automation, monitoring.  

Good to have Skills

• Hands-on experience with AWS and/or Azure cloud services for data science workloads 
• Understanding and experience with Kubernetes and Docker 
• Experience setting up and maintaining CI/CD pipelines for ML deployments.  
• Ability to write and maintain unit tests, integration tests, and validation tests for ML pipelines and APIs 
• Prior work on platform architecture for multi-tenant ML workflows