AI Machine Learning Engineer

NiyamIT•Ashburn, VA

9h•Hybrid

About The Position

Niyam is seeking an AI/ML Engineer to join our team in support of our work with a federal client. This role will focus on designing, developing, and deploying advanced AI/ML solutions that enhance operational efficiency, enable data-driven decision-making, and support evolving mission needs. The ideal candidate brings a strong foundation in machine learning, data engineering, and MLOps, along with experience working in secure, regulated environments. This position requires collaboration across cross-functional teams and a commitment to delivering scalable, ethical, and compliant AI solutions. We offer competitive compensation and benefits. This full-time position will be hybrid to Ashburn, VA. This position is contingent upon award of contract.

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.
Proven 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.
Local to Ashburn, VA and available to work onsite as needed.

Nice To Haves

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.

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).