ML Engineer

Who we are:

Makpar is a comprehensive professional and technical solutions provider for the Federal government. We combine functional and technical expertise in cloud engineering, data management, cybersecurity and emerging technologies to deliver mission success. We build the right IT solution for government clients by partnering with them to understand their WHAT, WHY, and HOW. Using our signature consulting methodology that we call “The Makpar Way,” we help agencies navigate the ongoing changes in the Federal technology landscape. We succeed where others fail because of our connected and engaged workforce are dedicated to delivering success for our clients and the American people.

Our Mission: We solve complex problems for the Federal government to accelerate access to citizen services.

When it comes to excellence, we deliver. Learn more about our employer brand at .

Makpar 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.

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.
• 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 NumPy.
• Hands-on experience with Databricks for data processing, model development, experimentation, and ML workflows.
• Experience with data preprocessing, feature engineering, data validation, and working with 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 effectively with cross-functional teams, including ML engineers, data engineers, software developers, and product stakeholders.
• Ability to work collaboratively in an Agile team environment.

Preferred Qualifications

• 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

Additional Requirements as per contract/client (Optional or Preferred):

• Candidates must be a US Citizen or a Legal Permanent Resident (Green Card status) for 3 years and be Federal Tax compliant.