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ML Engineer

Job in San Antonio, Bexar County, Texas, 78201, USA
Listing for: iQuasar, LLC
Full Time position
Listed on 2026-10-31
Job specializations:
  • Software Development
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 110000 - 180000 USD Yearly USD 110000.00 180000.00 YEAR
Job Description & How to Apply Below
Benefits:
  • 401(k)
  • Dental insurance
  • Health insurance
  • Paid time off
  • Vision insurance

iQuasar LLC has an exciting opportunity for an ML Engineer to join our growing team. The ML Engineer is responsible for developing, implementing, and optimizing machine learning models and data-driven solutions. Works closely with data engineers, developers, and stakeholders to prepare data, train and evaluate models, integrate ML capabilities, and support model deployment, monitoring, and continuous improvement.

We are seeking an ML Engineer for a contract position.

  • Position: ML Engineer
  • Position Type: Full-time-Remote
Role

Specific Duties /

Role Responsibilities:
  • Solve highly complex technical problems using Appian and supporting platforms
  • Mentor junior developers and conduct peer code reviews
  • Design and develop business logic, process models/flows, and UI screens in a BPM-based system
  • Provide technical leadership to Agile development teams
  • Design architectural frameworks using industry-standard methodologies
  • Resolve systems integration issues and present technical material to senior client leadership
Machine Learning Development & Optimization:
  • Develop, train, test, and optimize machine learning models to support predictive analytics, classification, and data-driven decision-making.
  • Evaluate model performance, accuracy, and reliability to identify opportunities for improvement.
  • Support the development and enhancement of scalable, reusable ML capabilities.
  • Data Engineering & Feature Development
  • Support data ingestion, preprocessing, normalization, validation, and feature engineering across multiple data sources.
  • Develop data enrichment capabilities to improve model performance and analytical insights.
  • Assist in building reusable data pipelines and preparing datasets for model training, evaluation, and inference.
ML Integration & CI/CD Support
  • Integrate ML models with data pipelines, APIs, and application services to support operational use cases.
  • Support model packaging, version control, deployment automation, and CI/CD pipeline improvements.
  • Assist with deployment, troubleshooting, and production readiness activities across development, test, and production environments.
Testing, Validation & Performance Monitoring
  • Develop and execute model testing and validation procedures to assess accuracy, reliability, and performance.
  • Monitor model outputs and identify data quality issues, defects, and opportunities for optimization.li>
  • Support ongoing model evaluation, maintenance, and continuous improvement.
Agile Team Collaboration
  • Collaborate with data engineers, software developers, architects, and product stakeholders to align ML solutions with business and technical requirements.
  • Translate business needs into ML experiments, technical designs, and implementation tasks.
  • Contribute to technical planning, solution design, and delivery of new ML capabilities.
Documentation & Knowledge Management
  • Document model architecture, experiments, evaluation results, deployment procedures, and technical decisions.
  • Maintain technical documentation and implementation guidance to support knowledge transfer and maintainability.
  • Share findings, best practices, and lessons learned to improve team effectiveness and consistency.
Required Qualifications:
  • Bachelor’s degree in Computer Science, Data Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related technical field.
  • Equivalent combinations of relevant education, technical training, and professional experience may be considered.
  • Experience developing, training, evaluating, and optimizing machine learning models using Python and common ML libraries such as scikit-learn (sklearn), pandas, and Num Py.
  • Hands‑on experience with Databricks for data processing, model development,…
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