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# ML Engineer

- **Company:** Tata Consultancy Services Limited
- **Location:** Irving, TX, United States
- **Salary:** $125,000.0 - $140,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Cloud Computing, Data Infrastructure, Distributed Systems, Apache Hadoop, Python (Programming Language), Machine Learning, OpenShift, Performance Tuning, Cloudera, Software Engineering, Cloud Platform System, Large Language Models, Pyspark, Information Technology, Machine Learning Operations
- **Published:** June 13, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1a873634f92ab1e2

## About the Role

Do you have experience in Design (software development lifecycle)?, Do you have a Bachelor's degree?, Strong experience in PySpark and Python to integrate with AI/ML Models.
Understanding of AI/ML Models and how they interface with OnPrem and Cloud environments.
Familiarity with ML Ops/LLM Ops and distributed systems.
Experience with Big Data platform like Cloudera Hadoop and Could platforms like AWS, GCP.
Solid understanding of system design patterns, scalability, observability, and performance tuning.
Strong analytical and problem-solving skills.
Passion for exploring and building with emerging technologies., Qualifications : BACHELOR OF COMPUTER SCIENCE

## Description

We are looking for a highly skilled and passionate Data Engineer with strong Python/PySpark experience and
understanding of AL/ML models to implement AL/ML models in OnPrem and Cloud environment.
In this role, you will be responsible for implementing AL/ML Models in different environments leveraging
Openshift/Kubernetics.
Responsibilities:
Work with data scientists/data engineers to translate prototypes and theoretical models into functional,
production ready code.
Collaborate with product and engineering team to implement AI/ML models using PySpark.
Understanding of AI/ML and large language models (LLMs).
Work on productionalize the AL/ML model in Hadoop environment.
Troubleshoot AI/ML Models post implementation to resolve issue raised by users.
Monitor model performance and continuously improve efficiency, accuracy, and user experience.


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