SR. DEVOPS ENGINEER and MLOps Engineer

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

• Fertility benefits: Not verified
• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Verification

Not verified checked Jun 13, 2026

Salary

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401(k) match

Reported from DOL Form 5500 industry filing (not employer-specific)

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

Not verified

Weekend work

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Company

Equity

Offered Verified - SEC 10-K source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

SR. DEVOPS ENGINEER and MLOps Engineer Job Country India At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world. Job Overview TE Connectivity is seeking a hands-on Machine Learning Engineer with 3-5 years of experience to help design, productionalize, scale, and optimize AI/ML solutions across the enterprise. This role will sit at the intersection of data engineering, data science, and ML engineering , and will focus on taking models from experimentation to reliable business use in production. The ideal candidate has strong experience with Databricks, AWS, MLflow, Python, PySpark, SQL , and modern MLOps practices. This person should be comfortable refactoring machine learning code and models , building robust production pipelines , setting up monitoring and alerts , improving model efficiency and reliability , and supporting both traditional ML models and Generative AI applications such as chatbots and agents . Responsibilities: Design, build, and maintain end-to-end machine learning pipelines for training, validation, deployment, monitoring, and retraining. Productionalize AI/ML models and ensure they are scalable, reliable, secure, and supportable in enterprise environments. Refactor existing machine learning models and codebases to improve performance, maintainability, reusability, and deployment readiness. Develop and manage model orchestration workflows across experimentation, batch scoring, real-time inference, and retraining c

Read the full description at careers.te.com. FewerJobs shows a preview and links to the original posting.