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Artificial Intelligence Machine Learning Engineer 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 ...

Cymertek

Machine Learning Engineer

San Antonio, TX · On-site

Machine Learning Engineer LOCATION San Antonio, TX 78208 CLEARANCE TS/SCI Full Poly (Please note ... If you are passionate about artificial intelligence, data-driven solutions, and continuously ...

TalentPros.AI

Staff Machine Learning Engineer

Austin, TX

$120K - $550K/yr

About the Team We are at the forefront of artificial intelligence, driving innovation and shaping ... We are looking for visionary Machine Learning Engineers to join our Applied Group, where you'll ...

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All JobsArtificial Intelligence Machine Learning Engineer JobsArtificial Intelligence Machine Learning Engineer Jobs in Texas

Artificial Intelligence Machine Learning Engineer information

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$29.3K

$120K

$180.3K

How much do artificial intelligence machine learning engineer jobs pay per year?

As of Jul 13, 2026, the average yearly pay for artificial intelligence machine learning engineer in Texas is $119,968.00, according to ZipRecruiter salary data. Most workers in this role earn between $94,600.00 and $144,400.00 per year, depending on experience, location, and employer.

What is an Artificial Intelligence Machine Learning Engineer?

An Artificial Intelligence (AI) Machine Learning Engineer is a professional who designs, builds, and implements machine learning models and AI systems. They work with large datasets, develop algorithms, and use programming languages like Python or R to enable computers to learn from data and make predictions or decisions. Their work is essential in fields such as natural language processing, computer vision, and robotics. These engineers collaborate with data scientists, software developers, and business stakeholders to deploy AI solutions in real-world applications.

What are some common challenges faced by Artificial Intelligence Machine Learning Engineers when deploying models to production?

One of the main challenges AI/ML engineers encounter is ensuring that models trained in a controlled environment perform reliably in real-world production settings. This often involves handling issues like data drift, scaling models to handle large volumes of requests, and integrating with existing infrastructure. Collaboration with data engineers and software developers is crucial to streamline deployment, monitor model performance, and address any unexpected behavior quickly. Keeping up with evolving tools and best practices is also important for long-term model maintenance and success.

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

AspectArtificial Intelligence Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, AI, ML, or related; certifications like TensorFlow, AWSBachelor's or higher in CS, Statistics, or related; certifications in data analysis or visualization
Work EnvironmentDevelops AI/ML models, coding, deploying algorithms in software environmentsAnalyzes data, builds models, interprets data insights for business decisions
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, marketing, consulting firms

While both roles involve working with data and algorithms, Artificial Intelligence Machine Learning Engineers focus on designing, building, and deploying AI/ML models in software systems. Data Scientists primarily analyze data to extract insights and support decision-making. The roles often overlap but differ in their core focus and daily tasks.

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

To thrive as an Artificial Intelligence Machine Learning Engineer, you need strong programming skills (typically in Python or R), a background in mathematics or statistics, and a degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch, or scikit-learn), cloud platforms, and relevant certifications are highly valuable. Problem-solving ability, creativity, and effective communication are important soft skills that distinguish top performers in this role. These competencies are crucial for designing robust AI solutions, collaborating with cross-functional teams, and driving innovation in rapidly evolving technological environments.
What are popular job titles related to Artificial Intelligence Machine Learning Engineer jobs in Texas? For Artificial Intelligence Machine Learning Engineer jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Artificial Intelligence Machine Learning Engineer jobs in Texas look for? The top searched job categories for Artificial Intelligence Machine Learning Engineer jobs in Texas are:
What cities in Texas are hiring for Artificial Intelligence Machine Learning Engineer jobs? Cities in Texas with the most Artificial Intelligence Machine Learning Engineer job openings:
Artificial Intelligence Machine Learning Engineer jobs near you
Infographic showing various Artificial Intelligence Machine Learning Engineer job openings in Texas as of July 2026, with employment types broken down into 81% Full Time, 15% Part Time, 3% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $119,968 per year, or $57.7 per hour.
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 18 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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