ML Engineer: Deploy & Scale Production Models

Exxon Mobil; Spring (TX); On-site; USD 130,000 - 190,000; Full time

Benefits offered by this job

Pension Plan

Savings Plan

Workplace Flexibility

Medical/Dental/Vision Plans

Wellbeing programs

Disability Plan

Job summary

ExxonMobil seeks a Machine Learning Engineer to lead the deployment and sustainment of data science work products at scale. You will coordinate cross-functional teams and apply software development, DevOps, and ML techniques to deliver production-grade solutions with automated pipelines and interfaces.

You will identify deployment designs, provision environments via Infrastructure as Code, implement CI/CD and monitoring, and mentor colleagues while collaborating with data scientists and business

Qualifications

• Experience delivering end-to-end ML deployments to production.
• Proficient in DevOps practices and data science pipelines.
• Ability to mentor junior team members.
• Strong collaboration with Data Scientists and business units.

Responsibilities

• Lead deployment and sustainment of data science products at scale.
• Coordinate cross-functional teams and apply software development, DevOps, and ML techniques.
• Build automated ML pipelines, retraining workflows, and user interfaces in partnership with Data Scientists and business units.
• Identify effective deployment designs and provision environments via IaC.
• Mentor colleagues and promote production-grade solutions across lines of business.

Skills

Software Development methodologies

DevOps toolsets

ML techniques

CI/CD

Mentoring

Business acumen

Tools

Infrastructure as Code

Job description

ExxonMobil seeks a Machine Learning Engineer to lead the deployment and sustainment of data science work products at scale. You will coordinate cross-functional teams and apply software development, DevOps, and ML techniques to deliver production-grade solutions with automated pipelines and interfaces.

You will identify deployment designs, provision environments via Infrastructure as Code, implement CI/CD and monitoring, and mentor colleagues while collaborating with data scientists and business