AI Machine Learning Engineer

About Niyam

Niyam was founded in 2007 by a group of consultants who share a unique vision: a technology company steeped in orderly process yet driven by passion and innovation. Over the following decade, we fine-tuned our craft and built an impressive track record of successful outcomes, securing our reputation as the go-to provider of smart, innovative solutions. Today, Niyam is at the forefront of the industry, leading the way in crafting mission-critical technologies for Emergency Preparedness & Response, Natural Resource Management, Law Enforcement & Justice, Health IT, and Global Citizen Services.

Roles and Responsibilities

• Design, develop, train, and validate advanced AI and machine learning models to support mission-critical use cases for a federal client, ensuring alignment with operational objectives and data governance standards.
• Evaluate and select appropriate machine learning techniques, algorithms, and neural network architectures (e.g., supervised, unsupervised, deep learning), leveraging frameworks such as TensorFlow and PyTorch to build scalable and efficient solutions.
• Perform end-to-end data lifecycle activities, including data collection, ingestion, cleansing, preprocessing, and feature engineering, ensuring data quality, integrity, and compliance with federal data management policies.
• Deploy AI/ML models into production environments, integrating with existing enterprise systems, cloud platforms, and APIs while ensuring high availability, scalability, and security.
• Establish and maintain MLOps pipelines to support continuous integration, continuous delivery (CI/CD), automated testing, model versioning, and performance monitoring across the model lifecycle.
• Monitor model performance over time, implement retraining strategies, and optimize models to ensure sustained accuracy, reliability, and operational effectiveness in dynamic environments.
• Identify, assess, and mitigate bias in datasets and model outputs, ensuring fairness, transparency, and adherence to ethical AI principles and applicable federal guidelines.
• Collaborate with cross-functional teams, including data engineers, software developers, cybersecurity personnel, and program stakeholders, to translate mission and business requirements into technical AI/ML solutions.
• Document model development processes, methodologies, and results to support auditability, reproducibility, and compliance with federal standards and accreditation requirements.
• Support security and compliance initiatives by aligning AI/ML solutions with frameworks such as NIST and FedRAMP, ensuring proper handling of sensitive data and adherence to system authorization processes (e.g., ATO).

Qualifications and Education Requirements

• US Citizenship with ability to obtain a Public Trust.
• Bachelor’s degree or higher in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical discipline from an accredited institution.
• 8+ years of progressive experience designing, developing, and deploying machine learning or artificial intelligence solutions within enterprise or mission-driven environments.
• Demonstrated experience with machine learning frameworks and tools such as TensorFlow, PyTorch, Scikit-learn, or similar technologies.
• Hands-on experience with data preprocessing, feature engineering, and working with large, complex datasets in both structured and unstructured formats.
• Prior experience deploying and operationalizing AI/ML models in production environments, including integration with cloud platforms (e.g., AWS, Azure, or GCP).
• Experience implementing MLOps practices, including CI/CD pipelines, model monitoring, versioning, and lifecycle management.
• Strong understanding of responsible AI practices, including bias detection, model explainability, and ethical AI considerations.
• Familiarity with federal security and compliance frameworks (e.g., NIST, FedRAMP) and experience working within regulated environments is preferred.
• Strong analytical, problem-solving, and communication skills, with the ability to effectively collaborate across technical and non-technical stakeholders.

Preferred Skills

• Experience supporting federal agencies or working within government contracting environments.
• Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
• Experience with big data technologies such as Hadoop, Spark, or distributed data processing frameworks.
• Familiarity with API development and microservices architecture.
• Experience implementing AI/ML solutions in cloud-native environments.