Job Description
As an AI Engineer, you will leverage statistical and AI/ML algorithms to deliver advanced analytics, including predictive analysis and modeling, pattern-of-life analysis, and remote sensing change detection. You will manage the entire AI/ML development lifecycle from design and training to deployment and maintenance while ensuring performance, observability, and security. This role contributes to building scalable infrastructure, real time dashboards, and automated pipelines that enable secure, compliant, and efficient AI operations aligned with mission and business goals.
Key responsibilities include:
Lead end-to-end data science initiatives, encompassing the design, training, and production deployment of AI/ML algorithms in IL5/IL6 environments.
Utilizing data pipelines in Databricks, Apache Spark, and related ETL technologies (e.g., AWS Glue, Apache Airflow).
Ensuring compliance with DoD security and accreditation standards, including STIGs and Impact Level controls.
Providing architectural oversight on data ingestion, curation, and storage to produce reliable, high-quality datasets for AI/ML development.
Supporting DevSecOps practices, CI/CD pipelines, and automation to streamline delivery.
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy: https://insightglobal.com/workforce-privacy-policy/.
Skills and Requirements
Bachelor's degree in Computer Science, Electrical Engineering, or a related technical field
Active Top Secret clearance with SCI Eligibility
5+ years of experience in software engineering, data engineering, and cloud architecture
Proven ability to conduct advanced data analysis and apply data science methodologies for predictive modeling, pattern of life analysis, and change detection analysis
Proficiency in Python, C++, and JavaScript
Proficiency in Linux -- system administration, scripting in Bash, troubleshooting
Strong expertise in AWS cloud services (compute, storage, networking, IAM)
Strong foundation in AI/ML algorithms and ability to implement agentic workflow, and prompt engineering
Experience in large language model (LLM) applications
Strong understanding of model evaluation metrics (e.g., precision, recall, AUC) and statistical drift detection methods
Expertise in containerization and orchestration (Docker, Kubernetes, OpenShift) and CI/CD automation (GitHub Actions, Jenkins)
Experience with time-series databases and relational databases
Proficiency in building and managing ETL pipelines (e.g. AWS Glue, Apache Airflow)
Strong communication skills with the ability to interface and collaborate with project managers, stakeholders, vendors, and technical staff - Active TS/SCI security clearance with Polygraph
Master's degree in Computer Science, Electrical Engineering, or a related technical field
Prior experience supporting DoD, Intelligence Community, or other U.S. Government programs
Understanding security accreditation processes for IL5/IL6 environments
Experience with the following:
Computer graphics and computer vision AI/ML
Remote sensing (SAR, EO/IR, MSI, etc.)
GIS
Principle component analysis (PCA)/linear discriminant analysis (LDA)
K-nearest neighbors (KNN)/Clustering techniques
Bayesian science/Bayes naive network
Reinforcement Learning (RLHF, PPO, DQN, etc.)
Variational auto encoders (VAEs)
Retrieval Augmented Generation (RAG) with Graph DB and Relational DB
Optimization algorithms
Kalman filtering
Network and RF communications
Signal processing
High performance computing (HPC)
PostgreSQL, Elastic, MongoDB, Graph DB for supporting data engineering workflows
Data governance, metadata management, and compliance frameworks