Data & ML Engineer

About this role

<div class="content-intro"><h3>About Us</h3> <p>Red Cell Partners is an incubation firm building and investing in rapidly scalable technology-led companies that are bringing revolutionary advancements to market in three distinct practice areas: healthcare, cyber, and national security. United by a shared sense of duty and deep belief in the power of innovation, Red Cell is developing powerful tools and solutions to address our Nation’s most pressing problems.</p></div><p>ABOUT DEFCON AI</p> <p>RESILIENCE IN THE FACE OF DISRUPTION. DEFCON AI is an insights company that leverages artificial intelligence, mathematical optimization, data analytics, and software engineering for resilient optimization of complex systems.<br>In today’s dynamically changing world, DEFCON AI’s technology aligns outcomes with operational goals, better decision making, and empowers customers to anticipate assess, and mitigate the impacts of disruptions.</p> <div> <p>&nbsp;</p> <p><strong><span data-contrast="auto">About the Role</span><span data-ccp-props="{}">&nbsp;</span></strong></p> <div> <p>As a Data &amp; ML Engineer you will build the data and model layer behind an AI-enabled decision-support system operating inside an accredited environment. That work covers ingestion from many source systems, resolution of incoming records against a shared data model, relevance scoring, and generation of explanations a user can act on and defend.</p> <p>Three characteristics make this a substantial technical challenge. The incoming data is predominantly low-signal, which means a model can report strong overall accuracy while failing on the cases that matter most. Every output must remain traceable to the underlying sources, because a person downstream is accountable for the result. Record matching is probabilistic rather than exact, so false matches and missed matches both carry meaningful cost.</p> <p>You will not be starting from an empty repository. We operate an established platform for source custody, extraction, and retrieval, and its architect is a member of this team, so existing design decisions are documented and accessible. Your work will focus on new capability rather than maintenance: record matching, calibrated scoring, and grounded generation, hardened for the target environment. We build with current tooling and expect the same, including the use of AI assistance in our own engineering practice.</p> <p>This is a fully remote role with occasional travel (up to 25%) to DEFCON AI HQ, customer sites, and vendor facilities as required.</p> <p><strong><span data-contrast="auto">Key Responsibilities</span></strong><span data-ccp-props="{"335559739":0}">&nbsp;</span></p> <p>The technical work falls into four areas. Deep expertise in all four is not expected, so please indicate where your depth lies when you apply. The engineering standards that follow apply to everyone on the team.</p> <p><strong>Data Modeling and Record Matching</strong></p> <ul> <li>Design and maintain the graph of entities, records, and the typed relationships between them</li> <li>Implement probabilistic matching, including blocking, candidate generation, pairwise scoring, clustering, and threshold policy</li> <li>Build deduplication and known-record suppression</li> <li>Establish provenance so that every node and edge traces to the source that asserted it</li> <li>Produce interface and data-flow design documentation detailed enough to serve as an implementation reference for other engineers</li> </ul> <p><strong>Scoring and Calibration</strong></p> <ul> <li>Develop relevance and priority models over large, imperfect record sets</li> <li>Own calibration and threshold design, establishing what a score means rather than only how it ranks</li> <li>Design abstention policy that routes uncertain and high-risk cases to a person rather than returning a confident answer</li> <li>Perform feature engineering, establish baselines before introducing complex models, and conduct error analysis that accounts for the differing cost of false positives and false negatives</li> </ul> <p><strong>Retrieval and Generation</strong></p> <ul> <li>Implement embeddings, vector storage, and retrieval across a large provenance-tracked evidence base</li> <li>Integrate language models through an approved managed service, and maintain a self-hosted or open-weight alternative within the same boundary</li> <li>Design prompts and output schemas</li> <li>Bind generated text to cited source records, and treat "insufficient evidence" as a valid system response rather than forcing a conclusion</li> <li>Own model packaging, serving, versioning, and rollback</li> </ul> <p><strong>Pipelines and Source Handling</strong></p> <ul> <li>Build secure ingestion, transformation, validation, and publishing across structured, semi-structured, and unstructured sources</li> <li>Implement quality checks, schema validation, lineage capture, and audit logging</li> <li>Establish source drift detection so that degradation is surfaced rather than carried into the analysis</li> <li>Generate statistically representative synthetic data so that development can proceed ahead of live data access</li> </ul> <p><strong>Engineering Standards</strong></p> <ul> <li>Work to the data model and standards set by the Data Lead, who approves designs and owns them through customer review</li> <li>Document assumptions, caveats, transformation logic, and known limitations, since deliverables are formally reviewed</li> <li>Instrument telemetry so that measurement does not require manual reconstruction</li> <li>Maintain the audit trail covering recommendations, human overrides, and model versions</li> <li>Submit model and pipeline changes through a gated release process rather than deploying in place</li> </ul> <p><strong>Required Qualifications</strong></p> <ul> <li><strong>5+ years</strong> of experience in data engineering, data architecture, applied machine learning, ML engineering, or production analytics engineering</l