About this opportunity
DEFCON AI, Inc. lists this Data & ML Engineer New Remote, USA opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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.
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.
About the Role
As a Data & 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.
Key Responsibilities
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.
Data Modeling and Record Matching
Design and maintain the graph of entities, records, and the typed relationships between them
Implement probabilistic matching, including blocking, candidate generation, pairwise scoring, clustering, and threshold policy
Build deduplication and known-record suppression
Establish provenance so that every node and edge traces to the source that asserted it
Produce interface and data-flow design documentation detailed enough to serve as an implementation reference for other engineers
Scoring and Calibration
Develop relevance and priority models over large, imperfect record sets
Own calibration and threshold design, establishing what a score means rather than only how it ranks
Design abstention policy that routes uncertain and high-risk cases to a person rather than returning a confident answer
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
Retrieval and Generation
Implement embeddings, vector storage, and retrieval across a large provenance-tracked evidence base
Integrate language models through an approved managed service, and maintain a self-hosted or open-weight alternative within the same boundary
Design prompts and output schemas
Bind generated text to cited source records, and treat "insufficient evidence" as a valid system response rather than forcing a conclusion
Own model packaging, serving, versioning, and rollback
Pipelines and Source Handling
Build secure ingestion, transformation, validation, and publishing across structured, semi-structured, and unstructured sources
Implement quality checks, schema validation, lineage capture, and audit logging
Establish source drift detection so that degradation is surfaced rather than carried into the analysis
Generate statistically representative synthetic data so that development can proceed ahead of live data access
Work to the data model and standards set by the Data Lead, who approves designs and owns them through customer review
Document assumptions, caveats, transformation logic, and known limitations, since deliverables are formally reviewed
Instrument telemetry so that measurement does not require manual reconstruction
Maintain the audit trail covering recommendations, human overrides, and model versions
Submit model and pipeline changes through a gated release process rather than deploying in place
Required Qualifications
5+ years of experience in data engineering, data architecture, applied machine learning, ML engineering, or production analytics engineering
Strong Python and SQL , with demonstrated experience working with large, imperfect operational data
Experience delivering systems for sustained operational use rather than exploratory analysis alone
Routine use of AI-assisted development, with informed judgment about where it adds value and where its output requires verification
Ability to explain a technical decision to a stakeholder who must defend that decision without understanding its internals
Active US Secret clearance. The work is performed in a controlled government cloud environment and requires a favorable investigation and CAC eligibility from the start
Elevated personnel security requirements apply to portions of this work and are discussed during screening
Willingness to travel up to 25% to customer sites, DEFCON AI HQ, and vendor facilities as required
Preferred Qualifications
Clearance: active Top Secret
Matching: direct experience applying probabilistic matching to inconsistent identity data, including names, dates, addresses, and identifiers, and familiarity with the failure modes of each. Record linkage, master data management, or identity management. Graph data modeling. PostgreSQL and pgvector or comparable. Graph algorithms applied in production
Modeling: model calibration and threshold design. Cost-sensitive learning where error types carry unequal consequences. scikit-learn, XGBoost, PyTorch
Retrieval and generation: retrieval-augmented generation in production. Prompt and output-schema design. Establishing that generated output remains grounded in its sources, and testing to confirm it. Self-hosted or open-weight model operation. Fine-tuning, adapters, or custom embeddings
Pipelines: AWS Glue, Airflow, dbt, Spark, Kafka, or NiFi. Unstructured and semi-structured document ingestion. Synthetic or representative test data generation
Environment: federal DevSecOps, RMF, ATO, or DoW cloud environments. Hardened base images. Experience advancing a pipeline from development through accreditation and deployment
Domain: sensitive federal or defense data, and work performed under privacy or comparable handling constraints
Responsible AI: documentation, model cards, fairness testing, and model monitoring. NIST AI RMF or comparable practice
What Success Looks Like
A data model that the rest of the team builds on without needing to redesign it
Matching decisions that can be explained and defended to a non-technical reviewer
Models whose miss rate is characterized, not only their overall accuracy
Generated explanations that assert no more than the sources support, with the citation path intact
Pipelines that surface problems early and trace them to a specific source
Consistent development progress, including during periods when live data is not yet available
What We Offer
A fully remote, results-based environment
Competitive salary, bonus, and equity package
100% employer paid, comprehensive health insurance including medical, dental, and vision for you and your family
Unlimited PTO, with your manager’s approval
Flexible work environment where you manage your work day
14 weeks of fully-paid parental leave
Salary Range
$150,000-$200,000. This represents the typical salary range for this position based on experience, skills, and other factors.
We’re an Equal Opportunity Employer: You’ll receive consideration for employment without regard to race, sex, color, religion, sexual orientation, gender identity, national origin, protected veteran status, or on the basis of disability.
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For government reporting purposes, we ask candidates to respond to the below self-identification survey. Completion of the form is entirely voluntary. Whatever your decision, it will not be considered in the hiringprocess or thereafter. Any information that you do provide will be recorded and maintained in a confidential file.
As set forth in DEFCON AI’s Equal Employment Opportunity policy,we do not discriminate on the basis of any protected group status under any applicable law.
If you believe you belong to any of the categories of protected veterans listed below, please indicate by making the appropriate selection. As a government contractor subject to the Vietnam Era Veterans Readjustment Assistance Act (VEVRAA), we request this information in order to measurethe effectiveness of the outreach and positive recruitment efforts we undertake pursuant to VEVRAA. Classification of protected categoriesis as follows:
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.