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AI/ML Engineer 2 - Intern - Location - Austin TX (Onsite) Need local to Austin TX

Conquest ConsultingAustin, TX🇺🇸United StatesPosted Oct 6, 2026

Quick Overview

Seniority
Entry Level
Work mode
On Site
Location
Austin, TX, United States
Posted
Yesterday
DockerAWSMachine LearningAirflowAzureGoogle CloudPythonREST

Job Description

Job Title: Artificial Intelligence/Machine Learning Engineer 2 - Intern
Service Term Expected: 12-month duration
Location: Austin, TX 78744
This role requires onsite presence 4 5 days per week.
Minimum Yrs of Experience, Skills, and Qualifications
Typically, 1-3 years of academic, internship, or entry-level experience in AI, data science, software engineering, or a related field
Possesses foundational knowledge of common concepts, tools, and practices
Works under guidance using established processes and standards
Does not typically exercise independent production decision making
Minimum Qualifications, Skills, and Experience
Education / Learning Background
Coursework toward or completion of a degree in Computer Science, Data Science, Engineering, Mathematics, or related discipline
Demonstrated interest in artificial intelligence, machine learning, and applied analytics
Technical Skills (Foundational / Developing)
Proficiency in Python
Familiarity with object-oriented programming concepts
Experience with version control
Exposure to data analysis, data migration, and basic model development
Understanding of basic software development and testing concepts
Preferred (Exposure or Academic Experience is Acceptable)
Data pipelines (e.g., Airflow, Prefect, or cloud-native equivalents)
Model deployment concepts (e.g., REST APIs, serverless patterns)
Cloud platforms or AI services (AWS, Azure, Google Cloud Platform, OCI)
Containerization concepts (Docker)
CI/CD fundamentals
Monitoring or model versioning concepts
Preferred Skills and Qualifications:
Preferred (Exposure or Academic Experience is Acceptable)
Familiarity with one or more of the following (hands-on or academic):
Data pipelines (e.g., Airflow, Prefect, or cloud-native equivalents)
Model deployment concepts (e.g., REST APIs, serverless patterns)
Cloud platforms or AI services (AWS, Azure, Google Cloud Platform, OCI)
Containerization concepts (Docker)
CI/CD fundamentals
Monitoring or model versioning concepts
Key Attributes for Success
Strong analytical and problem-solving skills
Ability to communicate technical concepts clearly to non-technical audiences
Willingness to learn and adapt in a fast-evolving technical environment
Attention to detail and commitment to data quality
Collaborative mindset and openness to mentorship and feedback

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