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Open nowPosted 390 days ago

Anthropont (Kiin) - Blackout AI Labs

rctsglobal.com36 open roles

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Your applicationOpen nowAnthropont (Kiin) - Blackout AI Labsrctsglobal.com · Remote

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This job: posted 390 days ago

The posting

About the job Blackout AI Labs is building the future of sales execution in the industrial space. We’re seeking elite 1099 contractors who can deliver real-world AI impact—fast. Projects include: AI-Driven Dynamic Pricing Engines Smart CPQ Quotation Generators Win-Rate Optimization Models Profitability Scoring Systems Competitor Quote Benchmarking Tools What We Offer: Work on high-impact ERP + AI integrations with measurable ROI. Structured SOPs & tool stack—you focus on execution, we provide the framework. Remote, flexible, outcome-driven engagements. A chance to shape the future of AI adoption in industrial SMBs. Who We’re Looking For: Experience with Snowflake / Databricks / BigQuery + dbt Skilled in Vertex AI / SageMaker / LLM integration Familiar with CPQ systems (Salesforce CPQ, PROS, Tacton) Strong ERP integration experience (NetSuite, SAP B1, Dynamics, Epicor, Odoo) Professionals who thrive in a fast-paced, execution-first environment This is not theory. This is AI that drives margins, speeds up quoting, and wins deals. If you’re a proven builder ready to deliver, apply now through Anthropont Talent Solutions. Blackout AI Labs – Cutting through hype to deliver impact.

The Ideal Contractor Profile for Blackout AI Labs 1. The “Builder-Strategist” Hybrid Not just a coder, not just a consultant — someone who can translate business pain (quoting, pricing, margin leakage) into a working AI/ERP solution. Think: someone who’s been frustrated at a big consultancy’s slowness, now wants to execute directly with speed. 2. ERP-Fluent + AI-Capable Has rolled up sleeves inside ERP/CPQ systems (SAP B1, NetSuite, Epicor, Dynamics, Odoo). Comfortable with data modeling (Snowflake/Databricks/dbt) and AI stack integration (Vertex AI, SageMaker, OpenAI APIs). Knows the difference between “neat model” and “production-ready integration.” 3. Outcome-Oriented Engineer Frames deliverables in terms of ROI: time saved, margin protected, conversion rate improved. Doesn’t just build; documents, transfers knowledge, and aligns outputs with business KPIs. 4. Independent but SOP-Disciplined Thrives as a 1099 contractor: owns their time, self-motivated, requires minimal babysitting. Willing to follow Blackout SOPs for QA, IP security, ROI framing, and knowledge transfer — not a cowboy coder. 5. Fast, Adaptive, Commercially Smart Can prototype quickly (90-day sprint) while keeping scalability in mind. Understands client perception — knows how to make the work look as good as it functions. Can flex between data science, integration, and product thinking.

In plain terms:You’re not looking for “AI researchers” or “ERP admins.” You want contractor-operators who can sit at the intersection of industrial ERP, data engineering, and applied AI — and who get fired up about turning messy quoting processes into profit-driving systems.

Would you like me to turn this into a candidate scorecard (with Must-Have / Nice-to-Have categories) so your team and Anthropont Talent can use it as a filter during interviews?

Blackout AI Labs – Contractor SOP Pack (For use with 1099 candidates via Anthropont Talent Solutions) Consistency & Quality Control SOP ● Ensure all deliverables meet Blackout AI Labs’ LaunchPad™ standards. ● Use provided templates, code libraries, and workflow checklists. ● Every project must include documented integration steps, annotated code, and QA sign-off. ● Peer review required before client delivery. ● Non-compliant work returned for rework at no extra cost. IP & Data Security SOP ● Protect client data and Blackout IP. ● Work only within approved, Blackout-controlled environments. ● No data stored on personal devices. ● NDA + IP assignment mandatory. ● Unauthorized tools or shadow IT = termination. Client Trust & Perception SOP ● Maintain unified, premium client experience. ● No direct client communication unless authorized. ● Use only Blackout AI Labs email and branding. ● Present as part of the BlackBox OS™ team. ● No discussion of other clients or side projects. Availability & Reliability SOP ● Ensure LaunchPad™ sprints run on time. ● Commit to Project Availability Agreement before starting. ● Flag delays 72 hours in advance with mitigation options. ● Backups may replace contractors if commitments aren’t met. ● Repeat unreliability = removal from pool. ROI Alignment SOP ● Tie technical execution to client business outcomes. ● Frame deliverables in ROI terms (time saved, margin protected). ● Use BlackBox ROI Alignment Form for each task. ● Deliverables without ROI context = incomplete. Legal & Compliance SOP ● Maintain 1099 compliance. ● Contractors control how, when, where they work. ● Compensation is project/milestone-based. ● Contractors provide own equipment/software. ● Blackout AI Labs may audit compliance anytime. Knowledge Transfer SOP ● Prevent knowledge gaps. ● Document all work in BlackBox Central Knowledge Hub. ● Submit handoff package: architecture, code notes, troubleshooting. ● Final payment contingent on full documentation delivery. Contractor Agreement Summary ● Deliver consistent, ROI-aligned work using Blackout standards. ● Protect client and company IP/data at all times. ● Operate independently as 1099 contractors, not employees. ● Ensure all work is documented and transferable. ● Represent Blackout AI Labs with professionalism and confidentiality.

Blackout AI Labs – Contractor Tool Stack & Use Case Guide For candidates applying to support AI LaunchPad™ ERP sales acceleration solutions. This document outlines the optimal tool stack and methodology for implementing AI LaunchPad™ use cases in the industrial SMB space. Contractors are expected to demonstrate expertise in these tools and approaches as part of Blackout AI Labs’ execution-first model. Cross-Cutting Stack (applies across all use cases) ● ERP Connectivity: Make.com Enterprise, Zapier for Enterprise, Workato, MuleSoft, or cloud-native iPaaS tools ● Data Platform: Snowflake, BigQuery, or Databricks Lakehouse with dbt for modeling ● Model Hosting: Vertex AI, AWS SageMaker, or Databricks Model Serving ● LLM Layer: OpenAI GPT-4o/4.1 for reasoning, quoting, and documentation ● BI & Dashboards: Power BI or Tableau ● Orchestration & Observability: Prefect, Airflow, Monte Carlo, or Soda ● Security & Identity: Azure AD/Okta, AWS Secrets Manager, Azure Key Vault AI-Driven Dynamic Pricing Engine ● Goal: Margin-optimized prices that react to cost and demand shifts. ● Build: Data feeds from commodity APIs, supplier price lists, ERP costs, and order history. Gradient-boosted or elastic net models for elasticity + margin/volume tradeoffs. Guardrails for min margin/max discount bands. ● Stack: Workato/MuleSoft for integration; Snowflake + dbt; Vertex AI or Databricks for modeling; GPT-4o for explanations. Smart Quotation Generator (AI-Powered CPQ) ● Goal: Quotes in minutes, not days. ● Build: CPQ core via Salesforce CPQ, PROS Smart CPQ, or Tacton CPQ. AI assist with GPT-4o for requirements parsing and proposal drafting. Feasibility check against ERP/MES. ● Stack: ERP link via Make.com/MuleSoft; Vector DB (pgvector/Pinecone) for retrieval; PandaDoc/DocuSign for e-sign; policy guardrails with OPA. Win-Rate Optimization Engine ● Goal: Predict close probability and recommend deal adjustments. ● Build: Features from customer segments, discounts, competitors, lead times, and history. XGBoost/LightGBM models with SHAP for explainability. ● Stack: Data in Snowflake + dbt; Vertex AI or SageMaker; Arize for monitoring; GPT-4o for sales-facing insights. Profitability Scoring for Custom Orders ● Goal: Flag unprofitable deals before quotes go out. ● Build: Simulator pulling BOM, routings, labor, setup, and scrap costs from ERP/MES. ML risk layer for one-offs, small-lot penalties, expedite freight. ● Stack: ERP/MES integration via MuleSoft; Databricks for cost simulation; CPQ workflow approvals; GPT-4o for rationale and alternatives. Real-Time Competitor & Market Quote Benchmarking ● Goal: Contextualize quotes vs. competitors/market. ● Build: Ingest competitor catalogs, distributor feeds, tender portals. Use GPT-4.1 with structured schema for normalization. ● Stack: ScrapingBee/Diffbot for acquisition; Snowflake/BigQuery for storage; Tableau/Power BI dashboards; CPQ sidebar with market deltas. Common Pitfalls to Avoid ● Avoid custom-rolling CPQ when PROS/Tacton already solve complexity. ● Avoid LLM-only pricing without guardrails. ● Avoid one-off scripts with no lineage/observability. ● Avoid direct ERP writes without staging/validation. Recommended Deployment Order (90-Day Sprint) Data foundation + governance CPQ uplift + Dynamic Pricing Win-Rate model + Sales Coaching Profitability Simulator Competitor Benchmarking

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