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September 4, 2025

Machine Learning Engineer

Develop optimized ML pipelines in secure, on-prem and hybrid environments.

Gurgaon, India
OnSite
Full Time
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Mail To: career@tracebloc.io

About the Role

We are seeking a capable and self-driven Junior Machine Learning Engineer with 3-4 years of experience and hands-on expertise in building and deploying production-grade ML systems. This role focuses on designing and maintaining scalable ML pipelines and workflows rather than only building isolated models.

Key Responsibilities

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

Required Experience & Skills

Skills:
  • Strong hands-on Python skills with ML libraries (scikit-learn, pandas, NumPy, PyTorch, TensorFlow)
  • Proven experience in building full ML pipelines (not just notebooks)
  • Solid understanding of production pipeline design patterns
  • Experience with Computer Vision and NLP pipeline development
  • Knowledge of production best practices: versioning, automation, monitoring
Good-to-Have Skills:
  • Hands-on AWS/Azure experience for ML workloads
  • Familiarity with containers and Docker
  • GPU pipelines setup for ML deployments
  • Experience with testing frameworks for ML pipelines and APIs
  • Knowledge of multi-tenant ML platform architecture

Why Join tracebloc?

  • Impactful Work: Shape how data scientists and AI teams collaborate securely
  • Growth Opportunities: Work with an experienced leadership team and cutting-edge tech
  • Competitive Compensation: Upper-market salary, performance incentives, and equity options
  • Vibrant Culture: Flexible working, diverse team, and creative autonomy in central Berlin

How to Apply

To help us understand your technical capabilities, include:

  • A link to your Git repository (GitHub, GitLab) with ML pipeline or deployment code
  • If no public repo, share sample code or a detailed project write-up (architecture, workflow, contributions)
  • CV

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