AI/ML Lead Engineer

Overview

Lead design, deployment, and governance of secure AI/ML systems for federal missions.

Key Responsibilities

• bias detection
• ml modeling
• model ci/cd
• secure etl
• model monitoring
• explainability

Tasks

-Implement bias detection and model fairness checks -Lead development of supervised, unsupervised, or reinforcement learning models for mission-specific applications (e.g., predictive analytics, NLP, computer vision, anomaly detection) -Lead the buildout of CI/CD for ML models — containerization (Docker), orchestration (Kubernetes), model versioning, automated retraining -Define technical architecture for ML systems (data pipelines, model training/inference, deployment) -Select and justify use of specific tools, frameworks, and compute environments (e.g., AWS GovCloud, Azure Gov, on-prem secure clusters) -Oversee or design secure ETL pipelines, data labeling workflows, and data quality processes -Monitor production systems and implement performance tracking, drift detection, and retraining triggers -Deploy models in secure enclaves or cloud environments that comply with government security posture -Handle classified or sensitive data under strict data governance and privacy rules -Ensure explainability and transparency — important for federal accountability (e.g., use SHAP, LIME, or interpretable ML techniques) -Work with structured/unstructured data from various government sources -Ensure compliance with federal IT/security standards (FedRAMP, NIST, DoD STIGs, etc.)

Requirements

• ml pipelines
• aws govcloud
• ts/sci
• docker/kubernetes
• data cataloging
• computer vision

What You Bring

-Knowledge of metadata management, data cataloging, and data lineage — crucial for auditability -Proven experience designing, training, and deploying machine learning and deep learning systems at scale -Bachelor's Degree in Computer Science, Computer Engineering or a related technical field and 8+ years of experience -Knowledge of classical ML, neural networks, NLP, computer vision, and statistical modeling -Familiarity with federal data privacy frameworks and compliance (FISMA, FedRAMP, NIST 800-53) -Expertise in secure data pipelines, ETL, and data quality controls for sensitive or classified datasets -Understanding of containerization, orchestration (Docker/Kubernetes), and infrastructure-as-code (Terraform, Ansible) -Understanding of AI model interpretability, bias mitigation, and model validation under federal ethics and transparency mandates -Deep familiarity with building production-grade ML pipelines (CI/CD, data versioning, automated retraining) -Experience with secure cloud (AWS GovCloud, Azure Gov, DoD Cloud IL levels) or on-prem HPC -Active TS/SCI clearance and be eligible for a Polygraph -Ability to choose and justify architectures (transformers, CNNs, LSTMs, etc.) based on mission needs -Active TS/SCI with Polygraph

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About Parsons Corporation

-Over decades it expanded into oil, petrochemical, water and infrastructure projects across the US and globally. -It leverages digital tools such as AI platforms, utility data systems, and drone integration alongside traditional design and construction. -Parsons completed high‑profile projects like major bridge, tunnel, and transit works. -Specializing in transportation, energy, environmental remediation, urban development, industrial, and cybersecurity projects. -The firm has delivered hundreds of mega‑projects worldwide, including infrastructure in the Middle East and environmental cleanup contracts.

Sector Specialisms

Security Clearance

-active ts/sci security clearance with polygraph required.