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Deidre Conrad

Deidre Conrad

Agency
USA flagShelton, Usa
$65.00/hrExpert1+SOC 2

Key Skills

Software

LabelboxLabelbox
CVATCVAT
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Top Subject Matter

No subject matter listed

Top Data Types

TextText
Geospatial Tiled ImageryGeospatial Tiled Imagery
ImageImage

Top Task Types

RLHFRLHF
Computer Programming/CodingComputer Programming/Coding
Red TeamingRed Teaming
Data CollectionData Collection

Company Overview

Reliable Solutions Northwest LLC — Corporate Profile Mission: To dominate high-margin data markets by transforming raw public information into actionable, high-value intelligence through aggressive automation and cost-efficient ETL workflows. Location and History: Based in the Pacific Northwest, RSNW was founded to bridge the gap between fragmented public records and enterprise-grade data products. We specialize in identifying market spreads where data asymmetry creates revenue opportunities. Services and Specialized Industries: Data Procurement — Large-scale scraping of public records and real estate. ETL Architecture — Custom pipeline development for data cleaning and normalization. Market Intelligence — Spread analysis and lead generation for high-margin sectors. Data Productization — Packaging raw datasets into APIs and reports. Tools and Methods: We utilize proprietary zero-waste scraping logic optimized for AWS architecture. By leveraging advanced selector identification and headless browser orchestration, we minimize compute costs while maximizing data throughput. Security and Workforce: Our workforce consists of elite data strategists and scraping engineers. We implement strict data integrity protocols and AWS-native security layers to ensure all harvested intelligence is stored and processed within an encrypted, compliant environment. Achievements and Clients: RSNW has successfully deployed automated systems that reduce data acquisition costs by up to 70%. We serve institutional investors, real estate firms, and data-driven startups looking for a competitive edge.

ExpertEnglish

Security

Security Overview

Reliable Solutions Northwest LLC: Security & Privacy Overview Security Framework & Compliance Reliable Solutions Northwest LLC (RSNW) operates on a Zero-Trust Architecture built within a hardened AWS environment. Our security posture is designed to meet the rigorous demands of AI training and enterprise data procurement. We align our workflows with SOC 2 Type II trust principles and the AWS Well-Architected Security Pillar. All technical operations are overseen by leadership holding CompTIA Security+ and AWS Certified Security credentials. Data Protection & Encryption We treat data integrity as a mechanical necessity. All client and harvested data are isolated within a private Virtual Private Cloud (VPC). At-Rest: Encryption via AWS KMS (AES-256) across all S3 buckets (e.g., my-data-pay-oregon) and EBS volumes. In-Transit: Mandatory TLS 1.3 protocols for all data movement, ensuring protection against intercept and man-in-the-middle attacks. Privacy & Anonymization RSNW employs strict Data Minimization and Privacy-Preserving techniques to safeguard sensitive information during the labeling and ETL process: PII Scrubbing: Automated scripts identify and redact Personally Identifiable Information before data enters the training pipeline. Pseudonymization: Real identifiers are replaced with reversible functional tokens to maintain data utility for RLHF and NER tasks without compromising privacy. Operational Controls Identity Management: We enforce Role-Based Access Control (RBAC) and Multi-Factor Authentication (MFA) for all system endpoints. Auditability: Every API call and system modification is logged via AWS CloudTrail, providing a tamper-proof audit trail for compliance verification. Proactive Defense: Internal red-teaming and vulnerability scanning are integrated into our deployment cycle to identify and mitigate threats before production.

Security Credentials

SOC 2

Labeling Experience

Labelbox

Technical RLHF and Red teaming

LabelboxComputer Code ProgrammingRLHFRed Teaming
Scope: Lead adversarial "Red Team" evaluation and RLHF alignment for a large language model (LLM) focused on technical and security domains. The project involved identifying logical vulnerabilities, prompt injection risks, and ensuring technical accuracy in generated code. Specific Tasks: Adversarial Prompting: Developing complex, multi-step prompts to stress-test model safety filters and ethical guardrails. RLHF Ranking: Evaluating and ranking model outputs based on a strict rubric of logical soundness, code efficiency, and security best practices. Code Debugging: Identifying and labeling security flaws (e.g., OWASP Top 10) in model-generated Python and Bash scripts. NER Classification: Manually tagging sensitive entities to prevent the leakage of PII or proprietary technical data. Project Size: Evaluated over 2,500+ complex technical prompts. Audited 1,000+ code blocks for functional and security compliance. Contributed to a high-priority model deployment for enterprise-grade security applications. Quality Measures: Multi-Pass Review: Every high-risk label underwent a self-audit and secondary verification against the project-specific security rubric. 0.98 Inter-Annotator Agreement (IAA): Consistently maintained top-tier alignment with expert consensus on complex ethical and technical edge cases. SOP Adherence: Strictly followed AWS Well-Architected Security Pillar guidelines during the handling and processing of all training datasets.

Scope: Lead adversarial "Red Team" evaluation and RLHF alignment for a large language model (LLM) focused on technical and security domains. The project involved identifying logical vulnerabilities, prompt injection risks, and ensuring technical accuracy in generated code. Specific Tasks: Adversarial Prompting: Developing complex, multi-step prompts to stress-test model safety filters and ethical guardrails. RLHF Ranking: Evaluating and ranking model outputs based on a strict rubric of logical soundness, code efficiency, and security best practices. Code Debugging: Identifying and labeling security flaws (e.g., OWASP Top 10) in model-generated Python and Bash scripts. NER Classification: Manually tagging sensitive entities to prevent the leakage of PII or proprietary technical data. Project Size: Evaluated over 2,500+ complex technical prompts. Audited 1,000+ code blocks for functional and security compliance. Contributed to a high-priority model deployment for enterprise-grade security applications. Quality Measures: Multi-Pass Review: Every high-risk label underwent a self-audit and secondary verification against the project-specific security rubric. 0.98 Inter-Annotator Agreement (IAA): Consistently maintained top-tier alignment with expert consensus on complex ethical and technical edge cases. SOP Adherence: Strictly followed AWS Well-Architected Security Pillar guidelines during the handling and processing of all training datasets.

2025 - Present

Market Intelligence & Web-Scraped Apparel Data

Internal Proprietary ToolingTextObject DetectionClassification
Analyzed large-scale web-scraped datasets from DataBoutique focusing on fashion retail (Lululemon, Nike). Performed data categorization and pricing analysis to identify market gaps and inventory trends for strategic arbitrage.

Analyzed large-scale web-scraped datasets from DataBoutique focusing on fashion retail (Lululemon, Nike). Performed data categorization and pricing analysis to identify market gaps and inventory trends for strategic arbitrage.

2024 - Present

Geospatial Data Curation & Real Estate Intelligence

Internal Proprietary ToolingGeospatial Tiled ImageryData Collection
Specialized in curating high-fidelity geospatial datasets, specifically targeting GSA leased properties and federal asset metrics. Managed the extraction and validation of regional physical asset data to create digital intelligence for market analysis.

Specialized in curating high-fidelity geospatial datasets, specifically targeting GSA leased properties and federal asset metrics. Managed the extraction and validation of regional physical asset data to create digital intelligence for market analysis.

2023 - Present
Data Annotation Tech

AI Model Evaluation and Text/Image Labeling

Data Annotation TechTextRLHFRed Teaming
Independent specialist in AI model evaluation, text and image labeling, and content moderation. Managed complex digital workflows and data annotation for search relevance, maintaining high consistency and quality for training model pipelines.

Independent specialist in AI model evaluation, text and image labeling, and content moderation. Managed complex digital workflows and data annotation for search relevance, maintaining high consistency and quality for training model pipelines.

2023 - Present