AI/ML Application Developer (A2R)

INFORMATION TECHNOLOGY STRATEGIES INC•Remote, US,

About The Position

Information Technology Strategies, Inc. (IT-Strat) is seeking an AI/ML Application Developer to support the Defense Agencies Initiative (DAI) program. This is a hands-on technical position responsible for designing, developing, and delivering artificial intelligence and machine learning capabilities directly within the Oracle E-Business Suite 12.2.x financial management environment hosted on Oracle Cloud Infrastructure (OCI). The client agency is the Defense Logistics Agency. The role involves designing and delivering AI/ML Functional and Technical Use Cases, developing an AI/ML and DevSecOps Transition Roadmap, and implementing production-grade AI/ML capabilities using OCI-native services. The developer will build and integrate REST API connections, apply natural language processing techniques to financial documents, and design cognitive science approaches to improve AI readiness and human decision-making support. Research and development efforts in training design, competency modeling, and cognitive metrics are also key. The position requires participation in Agile ceremonies, authoring user stories, and delivering working AI/ML capabilities within iteration cycles. The developer will also identify and remove technical impediments, align AI/ML architecture with broader systems architecture, contribute to transition plans, and support the integration of AI model deployment into the CI/CD pipeline.

Requirements

Eligibility to obtain a U.S. Government SECRET / TOP SECRET Clearance. (Basic Federal Clearance requirements are U.S. Citizenship, clear criminal history check, no recent or pending bankruptcies)
Five (5) years minimum experience in AI/ML development applied to enterprise software systems (PWS minimum).
Demonstrated experience with artificial intelligence, machine learning, natural language processing, knowledge discovery, and data mining applied to real operational datasets.
Proficiency in Java, C/C++, or Python with production-level code in at least one of these languages.
Experience in one or more of the following: natural language processing, text understanding, classification, pattern recognition, recommendation systems, targeting systems, or ranking systems.
Oracle Cloud Infrastructure AI and data science services, or equivalent major cloud provider AI platform experience with ability to transition to OCI.
REST API design and implementation for integrating ML models into enterprise application workflows.
End-to-end ML lifecycle experience from data preparation through model training, evaluation, deployment, and monitoring in a production environment.
Experience with unstructured data analytics and text-based information extraction from enterprise document repositories.
Agile or SAFe development experience with demonstrated ability to deliver working software within time-boxed iterations.
Oracle Cloud Infrastructure AI and data science services, or equivalent major cloud provider AI platform experience with ability to transition to OCI.
REST API design and implementation for integrating ML models into enterprise application workflows.
End-to-end ML lifecycle experience from data preparation through model training, evaluation, deployment, and monitoring in a production environment.
Experience with unstructured data analytics and text-based information extraction from enterprise document repositories.
Agile or SAFe development experience with demonstrated ability to deliver working software within time-boxed iterations.

Nice To Haves

Agile OCI AI Foundations or OCI Data Science Professional certification, demonstrating platform-specific knowledge on the environment where DAI operates.
AWS GovCloud experience with SageMaker, Bedrock, or equivalent managed AI services, providing versatility across the cloud platforms the government has approved for this program.
Working knowledge of DoD AI Ethics Principles and OMB AI governance guidance, which prevents building capabilities that stall in policy review.
NLP experience specifically applied to financial or accounting documents, including transaction narrative classification, posting rule extraction, or audit finding analysis.
Prior experience on DAI, GFEBS, LMP, or another DoD ERP financial system, eliminating weeks of program context onboarding.
SAFe 5 or 6 certification at Practitioner level or above, reflecting formal Agile methodology training rather than claimed team exposure.
Experience producing federal government deliverables under a QASP performance-based contract structure.

Responsibilities

Design and deliver the AI/ML Functional and Technical Use Cases document, covering OCI-native AI service implementation for financial reporting automation, user experience optimization, and operational efficiency gains across the DAI ERP environment.
Develop the AI/ML and DevSecOps Transition Roadmap laying out the phased approach for embedding AI capabilities into the DAI program and transitioning Agile processes from contractor to government ownership.
Implement production-grade AI/ML capabilities using OCI-native services including OCI Data Science, OCI Language, and OCI Vision, with consideration for AWS GovCloud AI services where applicable per government direction.
Build and integrate REST API connections between AI/ML models and Oracle EBS business processes, enabling automated decision support within financial workflows without requiring manual data extraction.
Apply natural language processing techniques to financial document processing, including audit report analysis, accounting narrative classification, and automated ATM documentation generation from source content.
Design and apply cognitive science approaches to improve AI readiness and human decision-making support within the DAI user community, focusing on how financial professionals interact with AI-augmented ERP processes.
Lead research and development efforts in training design, competency modeling, and cognitive metrics as they apply to AI adoption within a federal financial management environment.
Develop cognitive performance metrics that measure how effectively DAI users adopt AI-assisted workflows, providing quantifiable evidence of AI value delivery for program reporting.
Apply knowledge discovery and data mining techniques to unstructured DAI operational data, identifying patterns that inform both audit remediation strategies and future automation priorities.
Participate fully in Program Increment planning, iteration planning, daily standups, and iteration reviews as an active member of the Agile Release Train.
Author well-formed user stories with precise acceptance criteria for AI/ML features, working with the Product Owner to prioritize backlog items against program objectives and technical dependencies.
Deliver working AI/ML capabilities within two-week iteration cycles, demonstrating functional outputs at each iteration review rather than theoretical progress.
Identify and remove technical impediments to AI adoption across the program, escalating architecture or infrastructure blockers to the Software Systems Engineer Lead for resolution.
Work directly with the Software Systems Engineer Lead (L1) to align AI/ML architecture decisions with the broader DAI systems architecture and OCI infrastructure design.
Contribute technical content to the Final Transition Plan and Knowledge Transfer Materials (Deliverables 4-11 and 4-12), ensuring incoming personnel can sustain AI/ML capabilities after the contract period.
Support the DevSecOps Engineer and DevOps Engineers in integrating AI model deployment into the CI/CD pipeline on DLA-approved DevSecOps platforms.