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Associate Artificial Intelligence Machine Learning Jobs in Texas

Confie

Machine Learning Engineer

Addison, TX · On-site +1

$110K - $130K/yr

... Artificial Intelligence (AI)/Machine Learning (ML) models Essential Duties & Responsibilities Research, analyze, support, and implement machine learning solutions on the Snowflake Cloud data ...

Deloitte

AI Solutions Architect

Austin, TX

$62.50 - $82.25/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... Engineer Associate, Microsoft Azure Data Scientist Associate, or Microsoft Azure Solutions ...

Deloitte

AI Solutions Architect

Dallas, TX

$62.25 - $82/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... Engineer Associate, Microsoft Azure Data Scientist Associate, or Microsoft Azure Solutions ...

Deloitte

AI Solutions Architect

Houston, TX

$60.25 - $79.25/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... Engineer Associate, Microsoft Azure Data Scientist Associate, or Microsoft Azure Solutions ...

Deloitte

AI Solutions Architect

San Antonio, TX

$56.75 - $74.75/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... Engineer Associate, Microsoft Azure Data Scientist Associate, or Microsoft Azure Solutions ...

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Associate Artificial Intelligence Machine Learning information

What are the key skills and qualifications needed to thrive as an Associate Artificial Intelligence Machine Learning professional, and why are they important?

To thrive as an Associate Artificial Intelligence Machine Learning professional, you need a solid background in mathematics, statistics, and computer science, typically with a relevant degree and familiarity with machine learning concepts. Proficiency in programming languages like Python or R, experience with frameworks such as TensorFlow or PyTorch, and knowledge of version control systems are commonly required. Strong problem-solving, analytical thinking, and effective communication skills help differentiate top performers in this role. These skills are crucial for developing accurate models, collaborating with multidisciplinary teams, and driving impactful AI solutions.

What is the difference between Associate Artificial Intelligence Machine Learning vs Data Scientist?

AspectAssociate Artificial Intelligence Machine LearningData Scientist
Required CredentialsBachelor's in CS, AI, or related; certifications in ML/AIBachelor's/Master's in CS, Statistics, or related; often advanced degrees
Work EnvironmentTech companies, R&D labs, startupsData-driven organizations, consulting firms, tech companies
Employer & Industry UsageAI/ML teams, product developmentData analysis, predictive modeling, business insights
Common Search & ComparisonYesYes

Associate Artificial Intelligence Machine Learning roles focus on developing and implementing AI/ML models, often with entry-level responsibilities. Data Scientists analyze data to extract insights, build predictive models, and support decision-making. While both roles require knowledge of programming and statistics, Data Scientists typically have more advanced degrees and focus on data analysis, whereas Associate AI/ML roles are more specialized in AI/ML model development.

What does an Associate Artificial Intelligence Machine Learning professional do?

An Associate Artificial Intelligence Machine Learning (AI/ML) professional assists with the development, testing, and deployment of AI and machine learning models. They typically work under the supervision of senior data scientists or machine learning engineers, helping to preprocess data, select algorithms, and evaluate model performance. Their responsibilities may also include writing code, analyzing results, and supporting the integration of models into applications or systems. This entry-level role is ideal for those who have foundational knowledge in AI/ML concepts and are looking to gain practical, hands-on experience in the field.

What are some common challenges faced by Associate Artificial Intelligence Machine Learning professionals in their first year, and how can they overcome them?

Associate AI/ML professionals often encounter challenges such as understanding complex datasets, adapting to rapidly evolving tools and frameworks, and bridging the gap between theoretical knowledge and practical application. Collaborating closely with senior team members, seeking mentorship, and participating in code reviews are effective ways to overcome these challenges. Additionally, staying updated with industry trends and continuously practicing model building on real-world problems can help associates gain confidence and accelerate their learning curve.
What are the most commonly searched types of Artificial Intelligence Machine Learning jobs in Texas? The most popular types of Artificial Intelligence Machine Learning jobs in Texas are:
What are popular job titles related to Associate Artificial Intelligence Machine Learning jobs in Texas? For Associate Artificial Intelligence Machine Learning jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Associate Artificial Intelligence Machine Learning jobs in Texas look for? The top searched job categories for Associate Artificial Intelligence Machine Learning jobs in Texas are:
Associate Artificial Intelligence Machine Learning jobs near you
Artificial Intelligence/Machine Learning Engineer Specialist

Artificial Intelligence/Machine Learning Engineer Specialist

Connect Tech+Talent

Austin, TX • On-site, Remote

$113K - $136K/yr

Other

Posted 12 days ago

Job description

Job Description Artificial Intelligence/Machine Learning Engineer Specialist Austin, Texas (Hybrid OR Fully Remote) Contract (40 hours per week - 990 Hours) Minimum: 6+ Years - Applied AI/ML pipeline development and deployment for large-scale data reconciliation programs; production experience building anomaly-detection, root-cause analysis, and exception classification models using PyTorch, Scikit-learn, and Azure Machine Learning in regulated financial or government environments 6+ Years - Azure data platform engineering including Azure Databricks, Azure Data Factory, Azure Synapse Analytics, and Delta Lake; demonstrated ability to design automated, auditable reconciliation workflows eliminating manual row- and aggregate-level validation across multi-terabyte datasets 10+ Years - Advanced T-SQL and PL/SQL development across SQL Server and Oracle including stored procedures, partition switching, columnstore indexing, and query optimization sustaining sub-second query response for high-volume ETL and dashboard workloads 6+ Years - Rule-based exception classification pipelines and prioritized work queue construction; experience translating 30+ stakeholder control scenarios (finance, actuarial, risk) into automated validation logic, acceptance criteria, and agile backlog items 4+ Years - Cloud-native ingestion pipeline engineering with Azure Data Factory, Azure Service Bus, and Azure Functions; schema validation, data lineage management with Azure Purview, and containerized micro-service deployment via Docker, AKS, and Git-based CI/CD 4+ Years - Production model monitoring and drift detection using Azure Monitor metrics and custom drift detectors; MLflow experiment tracking and gradient-boosting ensemble tuning ensuring validation models retain statistical power across evolving data volumes and product mixes Preferred: 4+ Years - Continuous data quality enforcement using Great Expectations and parameterized pytest suites; experience validating 100+ reconciliation rules on synthetic and production samples with automated regression coverage for SOX, PCI-DSS, or HIPAA-regulated audit environments 3+ Years - Legacy system data migration experience involving COBOL or mainframe source environments (AWS Glue, Redshift, or equivalent); aggregate validation checks, tolerance-threshold variance surfacing, and actuarial or regulatory sign-off workflows for government or healthcare modernization programs 3+ Years - Azure Purview data lineage and metadata management; Delta Lake compaction, ACID semantics, and Parquet optimization for downstream analytics; Azure Key Vault managed identity integration for encryption-in-transit and at-rest compliance across reconciliation artifacts

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