Site Reliability Engineer II

Launch Your AI/ML Career as a Machine Learning Engineer

Are you passionate about the foundational elements of machine learning and eager to contribute to innovative AI solutions within a collaborative, supportive team environment? This role offers an exceptional entry point to build a career in AI/ML engineering, working directly alongside seasoned data scientists and engineers to solve real-world industry challenges using cutting-edge technologies.

What Makes This Role Great:

This role provides a structured environment for developing core competencies in machine learning, data engineering, 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 practices like containerization and automated monitoring. It is a premier platform for an ambitious technologist to translate academic theory 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.
• Data Engineering & Preprocessing: Contribute to data collection pipelines, feature engineering, and data cleaning tasks to ensure high-quality training sets tailored to industry use cases.
• Code Quality & System Testing: Perform routine debugging, unit testing, and comprehensive code documentation to ensure reliability, reproducibility, and high software standards.
• Model Integration & MLOps: Support the integration of trained ML models into existing enterprise applications, learning foundational MLOps workflows including containerization, deployment pipelines, and post-deployment performance monitoring.
• Cross-Functional Collaboration: Partner with senior engineers and data scientists 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.
• Data Structures & Problem Solving: Solid understanding of fundamental data structures and basic data modeling concepts, with the ability to follow structured engineering methodologies to solve well-defined problems.

Bonus Points For:

• Practical familiarity with standard deep learning frameworks such as PyTorch or TensorFlow.
• Baseline understanding of cloud environments (AWS, Microsoft Azure, or GCP).
• Hands-on experience with version control systems (Git/GitHub) and basic Linux/Bash scripting.
• A strong academic portfolio or GitHub profile showcasing practical ML/data science projects.
• Academic exposure to generative AI concepts, transformer architectures, or Large Language Models (LLMs).