tracebloc

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 

We are seeking a highly capable and self-driven Junior Data Scientist with 3–4 years of experience to take full ownership of building a production-grade ML platform — not just individual models. This role is pipeline-centric, focusing on designing, implementing, and maintaining robust data science workflows that enable scalable, repeatable, and automated model training and deployment. 

This is an on-site role based in Gurgaon where you will independently architect, build, deploy, and maintain the entire ML infrastructure. 

Key Responsibilities 

* Independently own and manage the full ML system lifecycle: from feature engineering and model development to production deployment and maintenance
* Design and build scalable, automated pipelines for data ingestion and processing, model training at scale, evaluation and monitoring, retraining workflows 
* Build and maintain a platform-oriented architecture enabling multiple ML workflows with extensibility and reuse in mind. 
* Deploy and operate ML workloads on AWS and/or Azure cloud infrastructure using containerized environments. 
* Implement robust CI/CD pipelines for automated testing, deployment, and rollback of ML services. 
* Write clean, production-grade, well-tested Python code supporting these workflows. 
* Maintain operational excellence for deployed pipelines: monitor performance, diagnose issues, and drive continuous improvement.

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 as engineered software systems (not notebooks or isolated scripts). 
* Practical experience with ML workflow orchestration tools (e.g., Airflow, Kubeflow Pipelines, SageMaker Pipelines preferred but not mandatory). 
* Deep understanding of pipeline design patterns and best practices for production environments. 
* Experience in building ML Pipelines for Computer Vision and NLP tasks. 

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

Data

Amazon

Data Science

Python

Testing

Azure

Cloud

Continuous Integration

NumPy

Docker

Software Development

Pandas

TensorFlow

Kubernetes

Infrastructure

Unit Testing

Design Pattern

Computer Vision

PyTorch

Azure PaaS

Scripting language

Scikit-learn

Airflow

Kubeflow

AWS SageMaker

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