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Remote Machine Learning Architect Jobs in Texas (NOW HIRING)

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Senior DevOps Engineer

Austin, TX · On-site +1

$128K - $165K/yr

Remote * Salary $150k - $200k * Seed Company * Skills: Ansible, Bash, Python, Terraform, Docker ... machine learning architecture. * Have an expert level understanding of Terraform and Ansible in ...

Vytwo

AI/ML Architect with Databricks , AWS

Prosper, TX · On-site +1

$60 - $70/hr

... machine learning platforms. The ideal candidate combines architectural thinking with strong ... engineering execution, demonstrating the ability to build modern lakehouse systems, optimize ...

Blue Cross of Idaho

AI Technical Lead

Meridian, TX · On-site +1

... system architecture, and technical leadership to build production-grade machine learning and ... remote within a mutually acceptable location. #LI-Hybrid Success Looks Like: * AI systems move ...

Performacentric

ML Engineer

Dallas, TX · On-site +1

Machine Learning Engineer (Llama AI Platform) Location: Remote (Preferred U.S. Time Zones ... Participate in architecture discussions and technical planning. * Contribute to AI solution design ...

Autodesk

$143K - $256K/yr

As a member of the COO architecture team, you will drive innovation and continually improve cloud ... Working knowledge of LLMs and machine learning, with an understanding of key concepts and hands-on ...

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All JobsRemote Machine Learning Architect JobsRemote Machine Learning Architect Jobs in Texas

Remote Machine Learning Architect information

How does a Remote Machine Learning Architect typically collaborate with distributed teams to deliver successful projects?

As a Remote Machine Learning Architect, effective collaboration with globally distributed teams is essential. You will often coordinate with data scientists, software engineers, and business stakeholders via virtual meetings, shared documentation, and project management tools. Regular communication, clear documentation of model designs, and version control practices are crucial to ensure alignment and smooth integration of machine learning solutions. Adopting agile methodologies and being proactive in addressing time zone differences help maintain project momentum and foster a productive team environment.

What is the difference between Remote Machine Learning Architect vs Data Scientist?

AspectRemote Machine Learning ArchitectData Scientist
Required CredentialsMaster's or PhD in CS, AI, or related fields; certifications in ML frameworksMaster's in Data Science, Statistics, or related; certifications in data analysis tools
Work EnvironmentDesigning ML systems, collaborating with engineering teams, remote or on-siteAnalyzing data, building models, often remote or in-office
Industry UsageTech, finance, healthcare, e-commerceResearch, finance, marketing, tech

Remote Machine Learning Architects focus on designing and implementing scalable ML systems, while Data Scientists analyze data and build models. Both roles require advanced degrees and often overlap in skills, but their core responsibilities differ in scope and focus.

What is a Remote Machine Learning Architect?

A Remote Machine Learning Architect is a professional who designs, builds, and oversees machine learning systems and infrastructure while working remotely. They collaborate with data scientists, engineers, and stakeholders to define system architecture, select appropriate algorithms, and ensure scalable deployment of machine learning models. Their responsibilities include setting technical standards, optimizing workflows, and ensuring integration with existing IT infrastructure, all accomplished through remote communication and collaboration tools. This role requires strong expertise in machine learning, cloud platforms, and software engineering.

What are the key skills and qualifications needed to thrive as a Remote Machine Learning Architect, and why are they important?

To thrive as a Remote Machine Learning Architect, you need deep expertise in machine learning algorithms, model development, and a solid background in computer science or related fields, often supported by an advanced degree. Familiarity with cloud platforms (such as AWS, Azure, or GCP), deep learning frameworks (like TensorFlow or PyTorch), and relevant certifications are typically expected. Strong problem-solving, communication, and project management skills help you collaborate effectively with distributed teams and stakeholders. These skills and qualities are crucial for designing scalable ML solutions that drive business value in a remote work environment.
What are popular job titles related to Remote Machine Learning Architect jobs in Texas? For Remote Machine Learning Architect jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Remote Machine Learning Architect jobs in Texas look for? The top searched job categories for Remote Machine Learning Architect jobs in Texas are:
What cities in Texas are hiring for Remote Machine Learning Architect jobs? Cities in Texas with the most Remote Machine Learning Architect job openings:
Remote Machine Learning Architect jobs near you
Infographic showing various Remote Machine Learning Architect job openings in Texas as of June 2026, with employment types broken down into 11% Internship, and 89% Full Time. Highlights an 56% In-person, 11% Hybrid, and 33% Remote job distribution.
Artificial Intelligence/Machine Learning Engineer Specialist

Artificial Intelligence/Machine Learning Engineer Specialist

Connect Tech+Talent

Austin, TX • On-site, Remote

Other

Posted 6 days ago

Job description

Job Description Artificial Intelligence/Machine Learning Engineer Specialist Austin, Texas (Hybrid OR Fully Remote) Contract 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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