AI/ML Platform Engineer

. Build internal platform tools and services, including self-service portals/workbenches, backend APIs using Python/FastAPI, automations, and CI/CD tooling .

Posted 9/17/2026full-timeSpring • Texas • United StatesSeniorLead💰 $147,050 - $230,850 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building internal platform tools and services, including backend APIs and automations using Python and FastAPI. Proficient in cloud resource management with AWS and Azure, and experienced in machine learning model productionization and integration.

Highest-signal resume keywords

Python DevelopmentAWS Certified Machine Learning SpecialtyTerraform ManagementMachine Learning IntegrationAgile Methodology

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonFastAPITerraformAWSAzureApache SparkC++JavaPyTorchScikit-learn

Soft Skills

CollaborationCommunicationProblem-SolvingProject Management

Tools & Technologies

SageMakerBedrockAzure ML/AI FoundryKubernetes

Certifications & Qualifications

AWS Certified Machine Learning Specialty

Industry Keywords

Artificial IntelligenceDeep LearningBig DataAlgorithmsData ScienceSoftware EngineeringNatural Language Processing

Tech Stack

Tools & technologies

ApacheAWSAzureJavaKubernetesPythonPyTorchScikit-LearnSparkTensorflowTerraformC++

About the role

Key responsibilities & impact

Build internal platform tools and services, including self-service portals/workbenches, backend APIs using Python/FastAPI, automations, and CI/CD tooling
Develop MCP/gateway integrations and AI-enabled automations and flows
Reduce friction for teams adopting the platform
Write and maintain Terraform; provision and configure platform resources across AWS and Azure
Diagnose deployment, networking, endpoint, and configuration issues
Partner with security and networking specialists
Participate in standups, syncs, planning and project meetings
Conduct design and architecture reviews and regular PR/code reviews
Onboard new teams and translate ambiguous requirements into practical plans
Support productionization of models across SageMaker, Bedrock, Azure ML/AI Foundry, and Kubernetes, including hosting, inference, scaling, cost, and operational readiness
Create documentation, onboarding guides, and reference examples
Identify and implement process and platform improvements proactively

Requirements

What you’ll need

Four-year or Graduate Degree in Computer Science, Statistics, Mathematics, Data Science, or another related discipline, or commensurate work experience or demonstrated competence
Typically 7-10 years of work experience, preferably in computer programming languages, machine learning, algorithms, statistical methods, or a related field
Preferred AWS Certified Machine Learning Specialty certification
Knowledge of Agile Methodology, Algorithms, Amazon Web Services, Apache Spark, Artificial Intelligence, Automation, Big Data, C++, Computer Science, Data Science, Deep Learning, Java, Machine Learning, Microsoft Azure, Natural Language Processing, Python, PyTorch, Scikit-learn, Software Engineering, and TensorFlow

Benefits

Comp & perks

Bonus and/or equity opportunities (United States of America candidates only)
Health insurance
Dental insurance
Vision insurance
Long term/short term disability insurance
Employee assistance program
Flexible spending account
Life insurance
4-12 weeks fully paid parental leave based on tenure
11 paid holidays
Additional flexible paid vacation and sick leave