ML Engineer

IQUASAR LLC

5h•$120,000 - $150,000•Remote

About The Position

iQuasar LLC is seeking an ML Engineer to develop, implement, and optimize machine learning models and data-driven solutions. The role works with data engineers, developers, and stakeholders to prepare data, train and evaluate models, integrate ML capabilities, and support deployment, monitoring, and continuous improvement. This is a full-time, remote contract position. The ML Engineer will also address complex technical problems using Appian and supporting platforms; design business logic, process models and flows, and UI screens in a BPM-based system; provide technical leadership to Agile teams; develop architectural frameworks; mentor junior developers; conduct peer code reviews; resolve systems integration issues; and present technical material to senior client leadership. The position supports the full ML lifecycle, including data preparation and feature engineering, model development and evaluation, integration with pipelines, APIs, and application services, CI/CD and deployment support, testing and production monitoring, Agile collaboration, and documentation of models, experiments, deployments, and technical decisions.

Requirements

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, pandas, and NumPy.
Hands-on experience with Databricks for data processing, model development, experimentation, and ML workflows.
Experience with data preprocessing, feature engineering, data validation, and structured and semi-structured datasets.
Familiarity with ML model deployment, REST APIs, and integration with data pipelines or application services.
Experience with Git and GitHub for source control, collaboration, and code management.
Experience developing or maintaining CI/CD workflows using GitHub Actions, including automated testing, build processes, and deployment pipelines.
Understanding of ML model evaluation metrics, testing methodologies, and model performance monitoring.
Experience writing clean, maintainable, well-documented Python code.
Strong analytical, problem-solving, troubleshooting, and communication skills.
Ability to collaborate effectively with cross-functional teams and work in an Agile team environment.

Nice To Haves

Relevant certifications or specialized training in machine learning, Databricks, cloud technologies, or MLOps.
Experience in government contracting, regulated industries, or large enterprises.
Familiarity with identity, access management, data platforms, or enterprise SaaS ecosystems.
Experience working with Jira and Confluence.
Candidates must be a U.S. citizen or a Legal Permanent Resident with Green Card status for 3 years and be Federal Tax compliant (listed as optional or preferred contract/client requirement).

Responsibilities

Solve complex technical problems using Appian and supporting platforms.
Mentor junior developers and conduct peer code reviews.
Design and develop business logic, process models and flows, and UI screens in a BPM-based system.
Provide technical leadership to Agile development teams and design architectural frameworks using industry-standard methodologies.
Resolve systems integration issues and present technical material to senior client leadership.
Develop, train, test, and optimize machine learning models for predictive analytics, classification, and data-driven decision-making.
Evaluate model performance, accuracy, and reliability and identify opportunities for improvement.
Support the development and enhancement of scalable, reusable ML capabilities.
Support data ingestion, preprocessing, normalization, validation, and feature engineering across multiple data sources.
Develop data enrichment capabilities and assist in building reusable data pipelines and preparing datasets for training, evaluation, and inference.
Integrate ML models with data pipelines, APIs, and application services.
Support model packaging, version control, deployment automation, and CI/CD pipeline improvements.
Assist with deployment, troubleshooting, and production readiness across development, test, and production environments.
Develop and execute model testing and validation procedures; monitor model outputs and identify data quality issues, defects, and optimization opportunities.
Support ongoing model evaluation, maintenance, and continuous improvement.
Collaborate with data engineers, software developers, architects, and product stakeholders to align 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.
Document model architecture, experiments, evaluation results, deployment procedures, and technical decisions; maintain implementation guidance and share findings and best practices.