ML Engineer

Makpar

About The Position

Makpar is seeking an ML Engineer to develop, implement, and optimize machine learning models and data-driven solutions. The role works closely with data engineers, developers, and stakeholders to prepare data, train and evaluate models, integrate ML capabilities, and support deployment, monitoring, and continuous improvement. The ML Engineer will solve complex technical problems, provide technical leadership and mentorship, and design business logic, process flows, user interfaces, and architectural frameworks. The role also supports data engineering and feature development, ML integration and CI/CD, testing and performance monitoring, Agile collaboration, and technical documentation. Work includes aligning ML solutions with business and technical requirements and supporting deployments across development, test, and production environments.

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, and well-documented Python code.
Strong analytical, problem-solving, troubleshooting, and communication skills.
Ability to collaborate 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.
US citizenship or Legal Permanent Resident (Green Card) status for 3 years and Federal Tax compliance, as specified as an additional optional or preferred contract/client requirement.

Responsibilities

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 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 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 ML solutions with requirements.
Translate business needs into ML experiments, technical designs, and implementation tasks.
Contribute to technical planning, solution design, and delivery of new ML capabilities.
Document model architecture, experiments, evaluation results, deployment procedures, and technical decisions; maintain implementation guidance and share findings and best practices.
Solve complex technical problems using Appian and supporting platforms.
Mentor junior developers and conduct peer code reviews.
Design and develop business logic, process models or 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.