I. Backend Development & AI Integration

• Architect and develop scalable backend services, APIs, and databases for AI applications.
• Integrate and deploy diverse AI/ML models (e.g., NLP, recommendation engines) into production systems via robust APIs.
• Optimize performance, security, and reliability. Develop data pipelines for efficient ingestion, storage, and retrieval of large datasets.
• Research and prototype AI-driven backend features for product innovation.

II. Program Management & Simulation Design

• Act as the primary Program Manager for all AI Simulation projects, ensuring timely delivery across client accounts.
• Co-design simulation content with Subject Matter Experts and Instructional Designers to align with business and learning objectives.
• Collaborate with development teams and product managers, contributing to high-level technical architecture decisions.

III. Platform Operations & Support

• Serve as the main Vendor Liaison for AI troubleshooting, maintenance, and feature coordination.
• Manage user accounts and conduct effective training for end-users and operations leaders.
• Provide real-time support, troubleshooting user login and simulation issues, and escalating complex technical problems.

IV. Reporting, Analytics, & Adoption

• Own the reporting cycle; validate usage reports for financial accuracy and invoice processing.
• Analyze and present detailed performance and usage KPIs (adoption, frequency, and outcome metrics) to demonstrate ROI.
• Drive ongoing platform adoption and explore expansion opportunities by partnering with key stakeholders.
• Maintain the AI Simulation Inventory and ensure accurate platform documentation.

Qualifications and Experience

• Backend & Tech Stack: Proven experience as a Backend Developer (Node.js, Python/Django/Flask) with strong skills in RESTful APIs, microservices, and SQL/NoSQL databases (MySQL, MongoDB).
• AI/ML: Hands-on experience with AI/ML tools (OpenAI API, Hugging Face, PyTorch) and cloud services (AWS/Google/Azure AI).
• Data & Project Management: Familiarity with data pipelines/ETL and strong ability to manage complex, parallel technical and operational timelines.
• Communication & Data Skills: Excellent communication to bridge technical and operational teams. Proficiency in data validation and analysis (e.g., Excel) and methodical troubleshooting.
• Advanced AI/DevOps: Experience with LLMs, MLOps, and DevOps practices (Docker, Kubernetes, CI/CD).
• Industry: Exposure to contact center, BPO, or L&D/tech-enabled training tools/platforms.