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 capable and self-driven Junior ML Engineer with 3–4 years of experience and hands-on experience in building and deploying production-grade machine learning systems. This role is pipeline-centric and ideal for individuals with at least 1 year of core ML/DL engineering experience , including experience in ML pipelines, workflow orchestration, and production deployment . You will be independently responsible for architecting, developing, and maintaining a scalable ML platform — not just individual models — enabling robust and reusable workflows. This is a full-time, 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 Please send your application to info@tracebloc.io , divyasingh@tracebloc.io , shujaat@tracebloc.io. 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 e xperience 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