Trimble is hiring a Site Reliablity Engineer II for its Construction Management Solutions team in Chennai, with the work focused on machine learning engineering and MLOps. The role is suited to early-career candidates with a bachelor’s degree in Computer Science, Mathematics, Engineering, Statistics or a closely related quantitative field. You will work on data pipelines, model experimentation, testing and the integration of ML models into enterprise applications. The role is worth considering for its direct mentorship from senior data scientists and engineers, along with practical exposure to real-world construction industry data and cloud-based AI products.
Company Overview
Trimble is a global technology company that connects the physical and digital worlds through positioning, modelling, connectivity and data analytics solutions. Its products serve industries including construction, geospatial, agriculture and transportation, while its Construction Management Solutions division provides cloud-based software and ERP solutions for contractors worldwide. Experience with Trimble can add enterprise technology, industry software and applied AI exposure to an early-career professional’s CV.
Job Details
| Company Name | Trimble |
| Job Role | Site Reliablity Engineer II |
| Qualification | Bachelor’s degree in Computer Science, Mathematics, Engineering, Statistics or a closely related quantitative field |
| Salary | Rs. 10-15 LPA (estimated) |
| Job Location | Chennai |
| Experience | Early-career professional |
| Employment Type | Full Time |
| Category | Freshers Job |
Role Overview
This role sits within Trimble’s Construction Management Solutions group and provides a structured entry point into machine learning engineering. The selected candidate will work with senior data scientists and engineers across the ML project lifecycle, from preparing industrial data to supporting deployment and monitoring.
- Work on foundational machine learning and data engineering activities for construction software solutions.
- Apply modern engineering practices such as containerisation, deployment pipelines and automated monitoring.
- Translate academic knowledge into reliable, scalable AI features for enterprise applications.
Eligibility & Qualifications
- Bachelor’s degree in Computer Science, Mathematics, Engineering, Statistics or a closely related quantitative field.
- The role is intended for an early-career professional.
- Strong conceptual understanding of machine learning principles, evaluation metrics, statistical methods and foundational deep learning concepts.
- Practical experience with data manipulation and algorithm design using Python libraries such as NumPy, pandas and scikit-learn.
- Applicants should be able to understand fundamental data structures and basic data modelling concepts and solve well-defined problems using structured engineering methods.
Key Responsibilities
- Assist in developing, testing and deploying foundational machine learning algorithms and models under the supervision of experienced practitioners.
- Explore advanced deep learning architectures as part of model experimentation activities.
- Contribute to data collection pipelines, feature engineering and data cleaning for industry-specific training datasets.
- Debug code, perform unit testing and maintain detailed documentation to support reliability and reproducibility.
- Support the integration of trained ML models into existing enterprise applications.
- Learn and apply foundational MLOps workflows, including containerisation, deployment pipelines and post-deployment performance monitoring.
- Work with senior engineers and data scientists to convert business requirements into clear and manageable ML tasks.
Skills Required
- Strong programming skills in Python.
- Working knowledge of NumPy, pandas and scikit-learn.
- Understanding of machine learning, statistical methods, evaluation metrics and basic deep learning concepts.
- Knowledge of fundamental data structures and basic data modelling.
- Problem-solving ability and familiarity with structured engineering methodologies.
- Practical familiarity with PyTorch or TensorFlow is an advantage.
- Baseline understanding of AWS, Microsoft Azure or Google Cloud Platform is an advantage.
- Hands-on familiarity with Git or GitHub and basic Linux/Bash scripting is an advantage.
- A strong academic portfolio or GitHub profile featuring practical machine learning or data science projects is an advantage.
- Academic exposure to generative AI, transformer architectures or Large Language Models is an advantage.
Why Join Trimble
Trimble offers direct exposure to real-world industry problems and structured learning under senior data scientists and engineers. The team highlights a collaborative and supportive work culture, flexible work arrangements and opportunities to build skills in machine learning, data engineering and MLOps. Its values of Belong, Grow and Innovate also emphasise inclusion, career development and employee ownership.
How to Apply for Trimble Hiring 2026
Eligible and Interested candidates can apply using the below-mentioned link before the link expires. Applicants need to register with the company portal in order to complete the registration process.
Apply Link: Click Here To Apply
