Description

This role provides a structured environment for developing core competencies in machine learning, data engineering, Databricks lakehouse operations, and data science under the direct mentorship of senior practitioners. Based in our Chennai office, you will gain hands-on exposure to real-world industrial data, contribute to the foundational stages of the ML project lifecycle, and learn modern MLOps and lakehouse practices like containerization, automated pipeline orchestration, and model monitoring. It is a premier platform for an ambitious technologist to translate academic theory and data skills into scalable, enterprise-grade AI products.
 

Key Exciting Responsibilities

• Model Implementation & Experimentation: Assist in the development, testing, and deployment of foundational machine learning algorithms and models under direct supervision, with opportunities to explore advanced deep learning architectures.
• Databricks & Data Engineering Pipelines: Build, maintain, and optimize data ingestion and transformation jobs on Databricks; support feature store curation, preprocessing, and feature engineering tasks to guarantee clean, high-quality training datasets.
• Code Quality & System Testing: Perform routine debugging, unit testing, and comprehensive code documentation across notebooks, scripts, and Delta Lake pipelines to ensure reproducibility and system reliability.
• Model Integration, MLflow & MLOps: Support the integration of trained ML models into existing enterprise applications using MLflow and standard containerized pipelines, tracking experiments, model versions, and post-deployment performance metrics.
• Cross-Functional Collaboration: Partner with senior data scientists, data engineers, and domain specialists to translate business requirements into clear, manageable ML tasks while adopting industry best practices.
   

Essential Skills & Experience

• Education: Bachelor's degree in Computer Science, Mathematics, Engineering, Statistics, or a closely related quantitative field (Trimble's Professional ladder typically requires four or more years of formal education).
• Foundational ML Literacy: Solid conceptual understanding of machine learning principles, evaluation metrics, statistical methods, and foundational deep learning concepts.
• Programming Proficiency: Strong coding skills in Python with practical experience in data manipulation and algorithm design using standard libraries such as NumPy, pandas, and scikit-learn.
• Databricks Operations & Data Handling: Working knowledge or hands-on academic/project experience operating within Databricks environments, including writing PySpark or Spark SQL queries, managing cluster runs, and handling datasets in Delta Lake tables.
• Data Structures & Problem Solving: Solid understanding of fundamental data structures, basic data modeling concepts, and software engineering discipline to solve well-defined analytical problems.
   

Bonus Points For:

• MLflow & Databricks Ecosystem: Practical familiarity with MLflow for experiment tracking, model registry, or Databricks Workflows/Jobs automation.
• Deep Learning Frameworks: Exposure to standard deep learning toolkits such as PyTorch or TensorFlow.
• Cloud Platforms: Baseline understanding of enterprise cloud platforms (Microsoft Azure, AWS, or GCP) and their managed data services.
• Development Tooling: Hands-on experience with version control systems (Git/GitHub), basic Linux/Bash scripting, and container basics (Docker).
• Applied Portfolio: Academic portfolio, capstone projects, or strong GitHub repositories demonstrating end-to-end data pipelines or machine learning applications.
• Generative AI: Academic exposure to transformer architectures, Large Language Models (LLMs), or retrieval-augmented generation (RAG) concepts

Key Skills

Education

Any Graduate