Job Posting: Senior AI Call Center Platform Developer
Budget: $3,000 – $5,000 USD
We're looking for an experienced developer or development team to architect and build an advanced AI-powered call center management solution. Our goal is to create an "intelligence layer" that connects and leverages insights from multiple platforms, going beyond what's possible with isolated systems for dialing, caller identity, agent analytics, or customer experience.
Stage 1 – Proof of Concept (PoC)
Goal
Demonstrate that integrating siloed call center data from multiple platforms can create actionable intelligence that is unavailable from any single source.
Scope
Multi-Platform Data Ingestion: Ingest a manageable sample dataset provided by our team, which will include, but not be limited to, data points such as customer lists, call recordings/transcripts, call logs, agent performance metrics, and DID reputation reports. This information will be sourced from various platforms, including those for hosted dialing, caller identity, agent analytics, and customer experience automation.
AI/ML Model for Cross-Platform Analysis: Build a compact AI/ML model to generate practical, actionable recommendations by extracting unique insights from a provided multi-platform dataset. The goal is to prove that valuable intelligence emerges when data is combined across systems. For example, the model could:
Derive Sophisticated Engagement Strategies by analyzing the relationship between conversational data (from transcripts) and customer history (from CRM) to recommend the most effective messaging for different audience segments.
Optimize Team Performance by correlating agent performance metrics (from an agent analytics platform) with customer satisfaction scores (from a customer experience automation platform) to suggest data-driven staffing or training adjustments to improve outcomes and customer sentiment simultaneously.
Refine Outreach Campaigns by blending caller identity reputation metrics (from a reputation management service) with campaign data (from a CRM or marketing automation platform) to identify which lead lists are most at risk of being flagged as spam and generate a new, prioritized outreach plan to improve long-term connect rates.
Basic Dashboard: Create a dashboard that displays these recommendations as actionable to-do lists or direct workflow items. Outputs must be more than simple reports.
AI-Powered Query Interface: Support natural-language management questions that require multi-platform intelligence. For example: “By analyzing call logs from a hosted dialer, DID reputation reports from identity management solutions, and agent performance analytics, which script produces the highest positive contact rate for Medicare callbacks after 3 PM?” The system must provide tailored, data-driven answers by merging insights from multiple platform types, not a single dataset.
Key Differentiator
While existing platforms provide insights in silos, this PoC must demonstrate recommendations and intelligence that emerge by combining and analyzing datasets integrated across multiple platforms.
Future Phases (Post-PoC)
Real-time, multi-platform data ingestion from CRM, dialers, conversational analytics, compliance, and reputation management systems.
Advanced cross-leveraged insights (e.g., blending DID trust scores, script performance analytics, outcome data, and agent coaching metrics).
Automated A/B testing for scripts and messaging spanning multiple platforms.
Deep native integrations with hosted dialing, agent analytics, workflow management, customer engagement, and compliance solutions.
Fully scalable SaaS product with robust reporting, multi-tenant architecture, and an open API framework.
Required Skills & Experience (PoC & Beyond)
Experience architecting AI-powered SaaS platforms or complex analytics solutions spanning multiple call center technology platforms.
Backend development expertise (Python, Node.js, etc.).
Advanced proficiency with LLMs (OpenAI, Anthropic, etc.) and speech-to-text APIs (Deepgram, Google, Whisper).
Strong data engineering skills (ETL, pipeline automation, cleaning, enrichment, multi-platform integration), with expertise in unified analytics platforms like Databricks.
Frontend/UI development in React, Angular, or Vue.
Cloud infrastructure expertise (AWS, Azure, GCP).
SQL/NoSQL database design, management, and cross-platform API development.
Expertise in multi-platform system integration (CRM, dialers, agent analytics, reputation management).
Familiarity with GDPR/CCPA compliance and role-based data authentication.
Exceptional communication skills, with the ability to explain technical design to stakeholders.
Screening Questions
Describe a prior project where you merged datasets (such as customer lists, call recordings, agent logs, reputation scores, and analytics) from multiple platforms to create actionable recommendations.
What experience do you have ingesting, aligning, and integrating complex data structures from various hosted dialing, identity management, agent analytics, and customer experience platforms?
How would you design and explain a scalable, secure, and privacy-compliant SaaS solution for voice and data products spanning multiple technology platforms?
Scenario: "Using call logs from a dialing platform, reputation scores from caller identity management, and agent performance data, how would you determine which agent script achieves the best outcomes for Medicare callbacks after 3 PM?" Explain your analytic approach, integration challenges, and decision logic. Your answer must leverage intelligence derived from multiple platforms, not just one system.
What is your ideal timeline and budget for the PoC phase?
How to Apply
Submit the following:
Cover letter and resume/CV.
Portfolio or case studies, especially those demonstrating multi-platform, cross-source insights.
Written responses to the screening questions above.
A high-level technical outline for the multi-platform PoC build.
Apply to help us pioneer this multi-platform intelligence layer and change the way call centers operate.
Stage 1 – Proof of Concept (PoC)
Goal
Demonstrate that integrating siloed call center data from multiple platforms can create actionable intelligence that is unavailable from any single source.
Scope
Multi-Platform Data Ingestion: Ingest a manageable sample dataset provided by our team, which will include, but not be limited to, data points such as customer lists, call recordings/transcripts, call logs, agent performance metrics, and DID reputation reports. This information will be sourced from various platforms, including those for hosted dialing, caller identity, agent analytics, and customer experience automation.
AI/ML Model for Cross-Platform Analysis: Build a compact AI/ML model to generate practical, actionable recommendations by extracting unique insights from a provided multi-platform dataset. The goal is to prove that valuable intelligence emerges when data is combined across systems. For example, the model could:
Derive Sophisticated Engagement Strategies by analyzing the relationship between conversational data (from transcripts) and customer history (from CRM) to recommend the most effective messaging for different audience segments.
Optimize Team Performance by correlating agent performance metrics (from an agent analytics platform) with customer satisfaction scores (from a customer experience automation platform) to suggest data-driven staffing or training adjustments to improve outcomes and customer sentiment simultaneously.
Refine Outreach Campaigns by blending caller identity reputation metrics (from a reputation management service) with campaign data (from a CRM or marketing automation platform) to identify which lead lists are most at risk of being flagged as spam and generate a new, prioritized outreach plan to improve long-term connect rates.
Basic Dashboard: Create a dashboard that displays these recommendations as actionable to-do lists or direct workflow items. Outputs must be more than simple reports.
AI-Powered Query Interface: Support natural-language management questions that require multi-platform intelligence. For example: “By analyzing call logs from a hosted dialer, DID reputation reports from identity management solutions, and agent performance analytics, which script produces the highest positive contact rate for Medicare callbacks after 3 PM?” The system must provide tailored, data-driven answers by merging insights from multiple platform types, not a single dataset.
Key Differentiator
While existing platforms provide insights in silos, this PoC must demonstrate recommendations and intelligence that emerge by combining and analyzing datasets integrated across multiple platforms.
Future Phases (Post-PoC)
Real-time, multi-platform data ingestion from CRM, dialers, conversational analytics, compliance, and reputation management systems.
Advanced cross-leveraged insights (e.g., blending DID trust scores, script performance analytics, outcome data, and agent coaching metrics).
Automated A/B testing for scripts and messaging spanning multiple platforms.
Deep native integrations with hosted dialing, agent analytics, workflow management, customer engagement, and compliance solutions.
Fully scalable SaaS product with robust reporting, multi-tenant architecture, and an open API framework.
Required Skills & Experience (PoC & Beyond)
Experience architecting AI-powered SaaS platforms or complex analytics solutions spanning multiple call center technology platforms.
Backend development expertise (Python, Node.js, etc.).
Advanced proficiency with LLMs (OpenAI, Anthropic, etc.) and speech-to-text APIs (Deepgram, Google, Whisper).
Strong data engineering skills (ETL, pipeline automation, cleaning, enrichment, multi-platform integration), with expertise in unified analytics platforms like Databricks.
Frontend/UI development in React, Angular, or Vue.
Cloud infrastructure expertise (AWS, Azure, GCP).
SQL/NoSQL database design, management, and cross-platform API development.
Expertise in multi-platform system integration (CRM, dialers, agent analytics, reputation management).
Familiarity with GDPR/CCPA compliance and role-based data authentication.
Exceptional communication skills, with the ability to explain technical design to stakeholders.
Screening Questions
Describe a prior project where you merged datasets (such as customer lists, call recordings, agent logs, reputation scores, and analytics) from multiple platforms to create actionable recommendations.
What experience do you have ingesting, aligning, and integrating complex data structures from various hosted dialing, identity management, agent analytics, and customer experience platforms?
How would you design and explain a scalable, secure, and privacy-compliant SaaS solution for voice and data products spanning multiple technology platforms?
Scenario: "Using call logs from a dialing platform, reputation scores from caller identity management, and agent performance data, how would you determine which agent script achieves the best outcomes for Medicare callbacks after 3 PM?" Explain your analytic approach, integration challenges, and decision logic. Your answer must leverage intelligence derived from multiple platforms, not just one system.
What is your ideal timeline and budget for the PoC phase?
How to Apply
Submit the following:
Cover letter and resume/CV.
Portfolio or case studies, especially those demonstrating multi-platform, cross-source insights.
Written responses to the screening questions above.
A high-level technical outline for the multi-platform PoC build.
Apply to help us pioneer this multi-platform intelligence layer and change the way call centers operate.