Who We Are

: Tria Federal delivers digital services and technology solutions that support the health and safety of veterans, service members and civilians. For two decades, federal agencies have relied on Tria companies to advance their critical missions and modernize their systems, so that they can uphold their commitment to the American people. Today, we are pushing the boundaries of possibility through partnerships and investments in artificial intelligence and emerging technologies, developing solutions for the biggest challenges that government will face tomorrow.

We are proud to employ and support military veterans who bring mission-first mindset, technical expertise, and leadership qualities that strengthen our work. Veterans, transitioning service members, and military spouses are strongly encouraged to apply. Job Description: A Senior Data Architect Engineer to support the DIA O&I Enterprise Integration and Assessments Data Team, focused on accelerating dataset understanding and delivering fit-for-purpose machine learning and data solutions.

Requirements

S. S. S.

Responsibilities

: Independently assess and explain datasets, including content, structure, quality, gaps, lineage, metadata, constraints, and known limitations Identify what is needed to make datasets ML-ready, including labeling, ground truth, feature creation, and data quality considerations Recommend practical approaches for addressing risk considerations such as bias, drift, and model limitations Design, build, and iterate ML models appropriate to the data and use case, such as classification, entity or record matching, anomaly detection, and natural language processing, as applicable Establish baselines and define evaluation metrics tied to operational utility Perform error analysis to guide model improvements and inform recommendations Build reproducible workflows that can be rerun and sustained within the customer’s operating environment and security constraints Support implementation decisions, including batch versus real-time processing, resource and cost tradeoffs, and latency or throughput considerations Use AWS SageMaker for experimentation and execution, including notebooks or Studio, training jobs, processing jobs, pipelines or automation, model registry, and deployment approaches as permitted Provide clear technica