Oxy
is seeking an experienced MLOps / AI Ops Engineer to support the deployment,
monitoring, and ongoing management of machine learning and advanced analytics
solutions across the organization. This individual will play a critical role in
bridging data science, cloud engineering, and operational support to ensure AI
and machine learning solutions are reliable, scalable, secure, and
production-ready.
The ideal candidate will have hands-on experience building and supporting MLOps
environments within AWS and will be responsible for operationalizing machine
learning models, creating automated deployment pipelines, and maintaining the
infrastructure required to support AI applications. This role will work closely
with data scientists, data engineers, software developers, and business
stakeholders to move machine learning solutions from development into
production while ensuring strong governance, monitoring, and performance
standards.
Key responsibilities include designing, building, and maintaining MLOps pipelines
and platforms for model training, deployment, monitoring, and retraining;
implementing CI/CD processes, model versioning, experiment tracking, and
automated testing; troubleshooting production issues related to models, data,
and infrastructure; and ensuring the reliability and observability of AI/ML
systems in production. The engineer will also be responsible for supporting
ongoing enhancements to existing AI platforms and helping establish long-term
operational support capabilities for the organization’s growing portfolio of
machine learning solutions.
Qualified candidates should possess at least five years of experience in MLOps,
AI Ops, data engineering, software engineering, or a related technical
discipline. Strong proficiency in Python and hands-on experience with AWS
services such as SageMaker, S3, EC2, EKS/ECS, Lambda, and CloudWatch are
required. Candidates should have experience deploying and supporting machine
learning models in production environments and be familiar with Docker,
Kubernetes, CI/CD tools, model monitoring, data drift detection, and machine
learning lifecycle management.
Strong communication and collaboration skills are essential, as this role will
partner with both technical teams and business stakeholders to support critical
AI initiatives. Experience in the oil and gas industry, particularly supporting
upstream operations, as well as experience with forecasting, time-series
analytics, infrastructure-as-code tools such as Terraform or CloudFormation,
and enterprise-scale AI platforms, is highly preferred.
This position is ideal for a hands-on engineer who enjoys solving complex
technical challenges, building scalable cloud-based AI solutions, and driving
operational excellence across machine learning platforms.
Job
Title
ML
Ops Engineer
Top
Skills Details
\*\*Candidate
MUST sit in Houston Tx. and go on-site Tuesday-Thursday at the Greenway
Plaza\*\*
AWS MLOps Expertise – Hands-on experience building, deploying, monitoring, and
supporting production ML solutions using AWS services such as SageMaker,
ECS/EKS, Lambda, S3, and CloudWatch.
Python & ML Production Engineering – Strong Python development skills for
production-grade ML workflows, automation, troubleshooting, and lifecycle
management.
CI/CD, Containers & Platform Operations – Experience with Docker,
Kubernetes, CI/CD pipelines, model monitoring, observability, and operational
support of ML systems in production.
Experience supporting machine learning platforms in a production environment.
Strong troubleshooting and root-cause analysis skills across AWS
infrastructure, data pipelines, and deployed ML models.
Experience with AWS SageMaker, including model deployment, monitoring, and
retraining workflows.
Experience with containerization technologies such as Docker.
Experience deploying and supporting workloads on Kubernetes, EKS, or ECS.
Knowledge of CI/CD tools and automated deployment practices.
Understanding of model drift, data drift, and model performance monitoring.
Experience with observability and monitoring tools, including CloudWatch.
Familiarity with infrastructure-as-code tools such as Terraform or AWS
CloudFormation.
Experience building scalable cloud-native applications and services.
Knowledge of software architecture, systems design, and engineering best
practices.
Experience supporting APIs and backend Python applications.
Understanding of data engineering concepts and data pipeline management.
Industry & Domain Experience
Oil & gas or energy industry experience, particularly supporting upstream
operations.
Experience with production optimization, drilling analytics, subsurface
modeling, or related operational systems.
Experience working with time-series data, forecasting models, or predictive
analytics.
Familiarity with operational technology (OT) or industrial data environments is
a plus.
Soft Skills & Professional Qualifications
Excellent verbal and written communication skills.
Ability to explain technical concepts to both technical and non-technical
stakeholders.
Strong collaboration skills with data scientists, engineers, architects, and
business teams.
Ability to operate independently with minimal oversight.
Strong problem-solving and critical-thinking abilities.
Ability to prioritize and manage multiple initiatives in a fast-paced
environment.
Customer-service mindset with a focus on reliability and support.
Interview
Information
1st
step will be a 30-minute screening call with the hiring manager. The 2nd and
final step will be a \"white boarding\" session ONSITE with 2 team
members.
Business
Challenge
Oxy
is building an internal MLOps support capabilities to operationalize, monitor,
maintain, and scale Oxy\'s growing portfolio of AI/ML solutions across multiple
business initiatives, reducing dependence on project-based vendors while
ensuring production AI systems remain reliable, supported, and sustainable
long-term
Information
Locations Position Open to Only localsIndustry Information TechnologyStatus OpenJob Age 22 Day'sCreated Date 07/15/2026No.of Positions 1Duration 12+Zip Code
