AI/ML Engineer

Parsons Corporation•USA VA (Virtual Office), VA

1d•$74,800 - $130,900

About The Position

Parsons is looking for an amazingly talented AI/ML Engineer to join our team! This role involves designing, developing, and deploying AI/ML models and tools for data mining, predictive modeling, and knowledge discovery to enhance chemical and biological threat characterization. The engineer will build and optimize automated data pipelines, integrate AI/ML capabilities into a secure, cloud-based data architecture, and develop model cards documenting model purpose, training data, performance metrics, limitations, and ethical considerations. The position requires implementing and monitoring model performance in real time, collaborating with cross-functional teams, and ensuring compliance with DoD cybersecurity, data privacy, and access control requirements. Additionally, the role involves developing tools for model explainability, participating in Agile sprints, and assisting with knowledge transfer and training activities.

Requirements

Active Secret or higher security clearance.
Bachelor’s degree in Computer Science, Data Science, Machine Learning, or a related technical field.
3-8 years of experience developing and deploying AI/ML models in production or enterprise environments.
Proficiency with AI/ML frameworks and libraries (e.g., LangChain, LlamaIndex, TensorFlow, PyTorch).
Experience building and maintaining data pipelines for machine learning, including data preprocessing, feature engineering, and model deployment.
Strong programming skills in Python or similar languages.
Experience with cloud platforms (e.g., AWS, Azure) and cloud-native AI/ML services.
Familiarity with RESTful APIs and integrating AI/ML models into larger software systems.
Understanding of data security, privacy, and access control best practices.
Experience working in Agile development environments and collaborating with multidisciplinary teams.
Strong analytical, problem-solving, and communication skills.

Nice To Haves

Master’s degree in Data Science, Machine Learning, or a related field.
Experience with large language models (LLMs), vector/semantic search, and advanced NLP techniques.
Familiarity with DoD cybersecurity and data governance standards (e.g., NIST SP 800-171, STIG, RMF).
Experience developing AI/ML solutions for knowledge graph construction and ontology-based reasoning.
Experience with model explainability, bias detection, and ethical AI practices.
Experience with containerization (Docker, Kubernetes) and secure model deployment.
Experience integrating AI/ML solutions in classified environments (e.g., SIPR, JWICS).
Prior experience supporting government or defense-related AI/ML projects.

Responsibilities

Design, develop, and deploy AI/ML models and tools for data mining, predictive modeling, and knowledge discovery to enhance chemical and biological threat characterization.
Build and optimize automated data pipelines for semantic/contextual chunking, named entity extraction, vector embedding, and relationship extraction.
Integrate AI/ML-enabled capabilities into a secure, cloud-based data architecture, ensuring interoperability with existing systems via standardized APIs and the Model Context Protocol (MCP).
Develop and maintain model cards documenting model purpose, training data, performance metrics, limitations, and ethical considerations.
Implement and monitor model performance, data/concept drift, and key metrics in real time; support model retraining and fine-tuning as new data becomes available.
Collaborate with data engineers, software developers, and stakeholders to define requirements, validate outputs, and support the creation of knowledge graphs and enriched data products.
Ensure all AI/ML workflows comply with DoD cybersecurity, data privacy, and access control requirements, including operation within isolated cloud environments.
Develop tools and dashboards for model explainability, transparency, and user interaction with AI/ML outputs.
Participate in Agile sprints, contribute to backlog refinement, and support documentation for AI/ML components.
Assist in knowledge transfer and training activities, including the development of tutorials and best practices for AI/ML usage.