Enterprise Tool Application and Analytics Developer (AI/ML Focus)

Chenega Corporation•Huntsville, AL

9h•Onsite

About The Position

The Enterprise Tool Application and Analytics Developer (AI/ML Focus) supports mission readiness by developing, integrating, and operationalizing data-driven applications and AI/ML-enabled analytics capabilities across Army enterprise tools, including but not limited to Army Vantage and Gabriel Nimbus. This role is a hands-on development position operating within an Agile environment, responsible for end to end solutioning to include building data pipelines, applications, and integrating AI/ML model use, and ensuring those capabilities are integrated into enterprise platforms to support Title 10 mission requirements.

Requirements

Bachelor’s degree in Computer Science, Data Science, Software Engineering, Artificial Intelligence, or related field.
5+ years of experience in software development, data engineering, analytics development, to include use of AI/ML.
Active Secret clearance with the ability to obtain TS with SCI eligibility.
Strong programming experience in: Python (required), PySpark, SQL
Experience developing data pipelines and performing data transformation/feature engineering.
Experience integrating analytics or AI/ML outputs into enterprise applications or platforms.
Ability to meet minimum clearance requirements.

Nice To Haves

3+ years’ experience supporting DoD or federal programs is highly desirable.
Demonstrated experience developing and deploying AI/ML models in production or operational environments.
Experience developing with Army Vantage, Palantir Foundry, Ionic, Maven or Apache Superset (e.g., data models, pipelines, analytics products, or dashboards).
Experience operating within an Agile development environment
Additional languages as applicable
DoD 8140/8570 baseline security certification (such as CompTIA Security+, CASP+, or CISSP) or a cloud-specific security certification (e.g., AWS Certified Security - Specialty)
Cloud-Specific ML Certifications such as Azure AI Engineer Associate (AI-102) or AWS Certified Machine Learning – Specialty
Ability to apply optimization techniques and search algorithms
Understanding of enterprise data environments and system integration.
Strong ability to develop and deploy AI/ML models and analytics solutions using appropriate tools and environments.
Clear understanding that enterprise tools such as Army Vantage are used to operationalize data and analytics, not serve as primary model development environments.
Experience integrating AI/ML outputs into enterprise workflows, dashboards, and decision-support systems.
Strong understanding of data pipelines, data architecture, and system integration patterns.
Ability to translate mission requirements into functional software and analytics solutions.
Strong understanding of Agile development methodologies and iterative delivery.
Familiarity with enterprise AI/ML environments such as Gabriel Nimbus or similar platforms.
Knowledge of RMF, cybersecurity requirements, and secure development practices within DoD environments.
Ability to evaluate model performance, reliability, and scalability in operational contexts.
Strong analytical and problem-solving skills, particularly in environments with incomplete requirements or evolving constraints.
Ability to operate effectively under ambiguity and develop solutions where processes may not yet exist.
Experience working with both structured and unstructured data across enterprise environments.
Excellent interpersonal communication, facilitation, and leadership skills to effectively coordinate across diverse stakeholders.
Proven ability to build and maintain trusted partnerships across government and mission organizations.
Skillful time management and organizational skills to manage competing priorities and deadlines.
Strong communication skills to translate technical concepts into mission-relevant insights.
Ability to work independently and within cross-functional Agile teams.
Ability to manage competing priorities and deliver under tight timelines.
Ability to foster collaboration, innovation, and continuous improvement across integrated teams.
Ability to work nights, weekends, and holidays as required.
Ability to travel up to 10%.

Responsibilities

Develop, utilize and deploy AI/ML models in appropriate environments (e.g., cloud platforms, data science environments) to support predictive and prescriptive analytics.
Build and maintain data pipelines (ETL/ELT) to support model training, data processing, and analytics applications.
Integrate AI/ML outputs into enterprise platforms and developed applications, including Army Vantage and Gaberiel Nimbus, to support operational decision-making.
Develop and maintain data models, analytics products, dashboards, and workflows within Vantage.
Translate mission requirements into technical solutions, including applications, data pipelines, and AI/ML use cases.
Participate in Agile development activities, including sprint planning, backlog refinement, daily stand-ups, and retrospectives.
Develop user stories, technical tasks, and acceptance criteria aligned to mission priorities.
Ensure data quality, integrity, and accessibility across enterprise systems and platforms.
Develop and support API integrations and data services across enterprise environments.
Collaborate with product owners, data engineers, and mission stakeholders to align solutions with operational workflows.
Implement secure development practices and ensure compliance with RMF, cybersecurity, and data governance requirements.
Evaluate and apply appropriate machine learning techniques (e.g., classification, regression, anomaly detection, time-series analysis) based on mission needs.
Monitor, validate, and maintain AI/ML model performance in operational environments.
Optimize application performance, scalability, and reliability across enterprise platforms.
Support integration and use of platforms for AI/ML development and deployment.
Develop and maintain technical documentation, including data flows, model descriptions, and system designs.
Support DevSecOps practices, including CI/CD pipelines for applications and model deployment.
Identify opportunities to improve mission effectiveness through data analytics automation, and AI/ML capabilities.
Support continuous improvement of development processes, analytics capabilities, and enterprise tool utilization.
Work across multi-functional teams, including with devsecops engineers, data engineers, mission assurance analysts and cybersecurity professionals
Other duties as assigned.