ML Engineer

Tata Consultancy Services; Conroe (TX); On-site; USD 110,000 - 140,000; Full time

Benefits offered by this job

Discretionary Incentive

Medical Coverage

Parental Leaves

Auto & Home Insurance

Commuter Benefits

Certification & Training Reimbursement

401K Plan

Job summary

Tata Consultancy Services in the United States seeks an experienced ML Engineer to design, build, and deploy scalable ML models and data pipelines in collaboration with Product and Data Science teams.

You will retrain, monitor, and optimize production systems, leverage AWS/Azure/GCP, and implement CI/CD for ML applications, ensuring governance and explainability. This role thrives in an Agile environment and focuses on delivering business impact.

Qualifications

• 1+ years of experience building, scaling, and optimizing ML systems.
• 1+ years of experience with data gathering and preparation for ML models.
• 2+ years of experience developing performant, resilient, and maintainable code.
• Experience deploying ML solutions in a public cloud (AWS/Azure/GCP).
• 3+ years of experience with distributed file systems or multi-node databases.
• 3+ years of experience building production-ready data pipelines for ML models.
• Experience designing and scaling data pipelines for ML models and evaluating performance.

Responsibilities

• Design, build, and deliver ML models and components with Product and Data Science teams.
• Inform infrastructure decisions with model selection, data and feature choices, training, tuning, validation.
• Develop and validate ML models, automate tests and deployment.
• Collaborate in an Agile team to enable state-of-the-art big data and ML apps.
• Retrain, maintain, and monitor models in production.
• Leverage cloud-based architectures to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Apply CI/CD, test automation and monitoring for deployment success.
• Ensure code quality, governance, and Responsible/Explainable AI practices.
• Use Python, Scala, or Java.

Skills

ML systems

Data gathering for ML

Production-ready pipelines

Cloud deployment

Distributed file systems

Programming (Python/Scala/Java)

Agile teamwork

Tools

AWS

Azure

GCP

Job description

Job Description

• 1+ years of experience building, scaling, and optimizing ML systems
• 1+ years of experience with data gathering and preparation for ML models
• 2+ years of experience developing performant, resilient, and maintainable code
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• 3+ years of experience with distributed file systems or multi-node database paradigms
• 3+ years of experience building production-ready data pipelines that feed ML models.
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance
• Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.
• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the‑art big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud‑based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure all code is well‑managed to reduce vulnerabilities, models are well‑governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
• Use programming languages like Python, Scala, or Java.

Must Have Technical/Functional Skills

• 1+ years of experience building, scaling, and optimizing ML systems
• 1+ years of experience with data gathering and preparation for ML models
• 2+ years of experience developing performant, resilient, and maintainable code
• Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform
• 3+ years of experience with distributed file systems or multi‑node database paradigms
• 3+ years of experience building production‑ready data pipelines that feed ML models.
• Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance

Roles & Responsibilities

• Design, build, and/or deliver ML models and components that solve real‑world business problems, while working in collaboration with the Product and Data Science teams.
• Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).
• Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.
• Collaborate as part of a cross‑functional Agile team to create and enhance software that enables state‑of‑the‑art big data and ML applications.
• Retrain, maintain, and monitor models in production.
• Leverage or build cloud‑based architectures, technologies, and/or platforms to deliver optimized ML models at scale.
• Construct optimized data pipelines to feed ML models.
• Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.
• Ensure all code is well‑managed to reduce vulnerabilities, models are well‑governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.
• Use programming languages like Python, Scala, or Java.

TCS Employee Benefits Summary

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support: Maternal & Parental Leaves.
• Insurance Options: Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
• Time Off: Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

Salary Range-$110,000-$140,000 a year