Senior AI/ML Engineer

Modern Government Solutions•NAS Point Mugu, CA

19h•Onsite

About The Position

Modern Government Solutions (MGS) is seeking a Senior AI/ML Engineer to design, build, and deploy advanced AI-driven capabilities supporting Blue Water Instrumentation (BWI) efforts at Point Mugu Sea Range. In this role, you will develop end-to-end AI/ML solutions, from data pipelines and models to operational applications, supporting telemetry analysis, decision-making, and real-world RDT&E events. You'll work across Azure Government and GCC High environments to deliver secure, scalable systems that integrate with operational workflows and support mission-critical outcomes. This position sits at the intersection of AI engineering, data infrastructure, and operational execution, ensuring capabilities are not only developed but successfully deployed and used in the field. You will collaborate with engineering, cybersecurity, and program teams to deliver compliant, production-ready solutions in a dynamic environment. Ideal for an engineer who wants to build real-world AI systems with direct mission impact, this role offers the opportunity to see your work used in operational settings.

Requirements

Must possess an active Department of Defense (DoD) Secret security clearance.
Bachelor's degree in Computer Science, Data Science, Machine Learning, Mathematics, or related field.
8+ years of experience in AI/ML engineering, data science, or applied machine learning, with experience supporting DoD or Federal environments.
Proven experience designing, building, and deploying production AI/ML systems, including models, pipelines, and end-to-end data workflows.
Demonstrated experience developing AI/ML solutions in Azure Government or equivalent FedRAMP High cloud environments.
Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face).
Experience building and deploying AI-driven applications, including automation, decision-support tools, or knowledge management solutions.
Hands-on experience with data pipeline engineering (e.g., Apache Spark, Azure Data Factory, Databricks) and large-scale data processing.
Experience with MLOps practices, including model versioning, experiment tracking, reproducibility, and containerized deployment (Docker, Kubernetes).
Familiarity with Azure AI/ML services (Azure ML, Azure OpenAI, Cognitive Services) and development within MS365 GCC High environments.
Understanding of DoD Cloud SRG impact levels (IL4/IL5/IL6) and secure AI/ML deployment constraints.
Experience working within cybersecurity and compliance frameworks (CMMC 2.0, NIST SP 800-53) and handling controlled data (PII/CUI).
Strong understanding of data governance, access controls, and secure data handling in classified or controlled environments.
Excellent analytical, problem-solving, and technical communication skills.
One or more of the following certifications (or equivalent demonstrated expertise): Azure AI Engineer Associate (AI-102) or Azure Data Scientist Associate (DP-100) AWS Machine Learning Specialty or equivalent cloud ML certification CompTIA Security+ CE (or higher, to satisfy DoD 8140 baseline if applicable)

Nice To Haves

Active DoD TS/SCI security clearance.
Master's degree in Computer Science, Data Science, Machine Learning, Mathematics, or related field.
Experience developing and deploying AI/ML solutions within Azure Government or GCC High environments, including use of Azure OpenAI Service and secure cloud AI capabilities.
Familiarity with DoD AI governance, including CDAO guidance, Responsible AI principles, and frameworks such as the NIST AI Risk Management Framework (AI RMF).
Experience building advanced AI applications, including Retrieval-Augmented Generation (RAG), knowledge management systems, or decision-support tools in secure environments.
Experience working with telemetry, sensor data, or real-time/streaming ML inference in test and evaluation (T&E) or operational environments.
Knowledge of DoD network environments (e.g., NMCI, FlankSpeed) and constraints associated with development and deployment in controlled systems.
Experience with MLOps practices, including model lifecycle management, experiment tracking, automated retraining, and model registries.
Familiarity with data governance, Power Platform controls, and DLP policies within GCC High or similar secure enterprise environments.
Experience supporting programs with controlled data requirements, such as FMS or other regulated data handling environments.
Relevant advanced certifications (e.g., Azure Solutions Architect Expert, Databricks Certified, TensorFlow Developer) or equivalent demonstrated expertise.

Responsibilities

Design, develop, and deploy AI/ML models supporting telemetry analysis, automated collection planning, anomaly detection, predictive maintenance, and decision-aid tools for RDT&E events.
Architect and implement end-to-end ML and data pipelines in Azure Government (AzureGov), including data ingest/ETL, labeling workflows, governance, and role-based access controls for experiment data.
Develop AI-enabled applications and automation solutions within MS365 GCC High and Azure environments, including intelligent document processing, workforce analytics, predictive staffing models, and natural language interfaces.
Implement advanced AI capabilities such as Retrieval-Augmented Generation (RAG), knowledge management solutions, and data visualization products (dashboards, COP views) to support operational decision-making and Government evaluations.
Ensure AI/ML systems comply with DoD AI Ethics Principles, NIST AI Risk Management Framework, and applicable cybersecurity requirements, collaborating with cybersecurity teams on RMF, STIG, and continuous monitoring compliance.
Establish MLOps practices, including model versioning, experiment tracking, reproducibility, and lifecycle management to support RDT&E repeatability and transition decisions.
Develop and maintain technical documentation, data/model traceability, and performance artifacts to support engineering requirements, Government reviews, and program deliverables.
Stay current on emerging AI/ML technologies, DoD AI initiatives, and secure cloud capabilities to inform solution development and program strategy.
Operate across office and laboratory environments, providing hands-on support to users and operational systems supporting AI/ML capabilities.
Support test events and operational activities as needed, including extended hours and up to 20% travel to meet program and mission requirements.