AI Specialist

Role Outline:

This role requires a unique combination of deep technical skills in backend development and AI integration, coupled with strong program management, stakeholder engagement, and operational support abilities. The Specialist/Engineer will be the technical owner, operational expert, and adoption champion for our AI Simulation projects.

Key Responsibilities:

I. Backend Development & AI Integration

Responsible for the technical architecture and implementation that underpins all AI-driven applications, ensuring performance and scalability.

• Design and Development: Design, develop, and maintain scalable backend services, APIs, and databases to support AI-driven applications. Implement best practices for clean, maintainable, and testable backend code.
• Performance and Optimization: Optimize server-side performance, security, and reliability across all AI systems.
• Model Integration: Incorporate, fine-tune, test, and deploy AI/ML models (e.g., recommendation engines, automation tools, NLP) into production backend systems. Build robust APIs to serve model outputs to frontend and third-party applications.
• Data Management: Develop efficient data ingestion and preprocessing pipelines for training and inference. Ensure proper storage, retrieval, and processing of large, complex structured and unstructured datasets.
• Research & Innovation: Stay updated on emerging AI technologies, frameworks, and tools. Propose and prototype AI-driven backend features to continuously improve product capabilities.

II. Program Management & Co-Design

Serve as the primary program owner, driving the strategic direction and content design of AI simulation projects in collaboration with non-technical partners.

• Program Leadership: Serve as the primary Program Manager for all AI Simulation projects across multiple client accounts, ensuring timely delivery and successful outcomes.
• Simulation Content Design: Partner closely with Instructional Designers and Subject Matter Experts (SMEs) to co-design simulation content that is accurately aligned with key business goals and learning objectives.
• Collaboration & Architecture: Work closely with frontend developers, other AI engineers, and product managers to align backend systems with project requirements. Participate in high-level architecture discussions and contribute to technical decision-making.

III. Platform Operations & Support

Manage the day-to-day operation, user base, and external vendor relationship for the AI simulation platform, providing critical real-time support.

• Vendor Liaison: Act as the main liaison with the AI Vendor, coordinating advanced troubleshooting, system maintenance, and feature requests.
• User Management & Training: Add, update, and manage users within the AI simulation platform. Conduct effective training for operations leaders and end-users on how to access, set up, and use the AI simulation tools.
• Troubleshooting: Troubleshoot user login and simulation issues in real time, escalating complex technical issues to the vendor or internal development teams as needed.

IV. Reporting, Analytics, & Adoption

Own the reporting cycle, translating raw data into actionable insights to drive adoption, demonstrate value, and support expansion strategies.

• Reporting & Validation: Pull, validate, and cross-reference usage reports (including reports from key stakeholders) prior to invoice processing to ensure financial accuracy.
• KPI Analysis: Gather and analyze detailed performance and usage KPIs (adoption, frequency, and outcome metrics) by account and site.
• Stakeholder Engagement: Partner with Procurement and Operations to drive ongoing adoption of the tool and explore expansion opportunities.
• ROI Demonstration: Present compelling data insights during client or internal meetings to clearly demonstrate the ROI and effectiveness of the AI tool.
• Inventory Management: Track and maintain the AI Simulation Inventory and ensure accurate documentation in the Application Inventory system.
• Engagement Strategy: Identify key end-user points of contact (POCs) and work directly with them to increase engagement and overall performance.

Qualifications and Experience:

• Proven experience as a Backend Web Developer utilizing modern frameworks , , Python/Django/Flask, or similar).
• Strong understanding of RESTful APIs, microservices architecture, and database systems (SQL like MySQL, NoSQL like MongoDB).
• Hands-on experience with AI/ML tools and libraries (e.g., OpenAI API, Hugging Face, TensorFlow, PyTorch).
• Knowledge of cloud-based AI services (AWS AI/ML, Google Cloud AI, Azure AI).
• Familiarity with data pipelines, ETL processes, and robust model deployment strategies.
• Strong project management skills with the ability to manage complex, parallel technical and operational timelines.
• Excellent communication skills; proven ability to bridge understanding between technical teams and operational stakeholders.
• Comfort and proficiency in data validation and reporting using tools like Excel, and the ability to analyze complex usage data.
• Exceptional problem-solving skills and the ability to troubleshoot tech or login issues methodically in a fast-paced, agile environment.

Key Competencies

• Detail-Oriented and Organized: Maintains accurate documentation and tracks multiple project elements simultaneously.
• Tech-Savvy and Solution-Oriented: Proactively identifies and implements technical or process solutions.
• Stakeholder Management: Effectively manages expectations and maintains positive relationships with both internal and external partners.
• Data Validation and Analysis: Possesses critical thinking to ensure data accuracy and draw meaningful conclusions from performance metrics.
• Trainer/Facilitator Skills: Confident in leading workshops and conducting effective training sessions for users at all levels.
• Preferred Qualifications & Bonuses
• AI/ML Experience: Experience with large language models (LLMs) integration and a strong understanding of MLOps practices.
• DevOps Bonus: Background in DevOps for AI-enabled backend systems and hands-on experience with containerization (Docker, Kubernetes) and CI/CD pipelines.
• Industry Experience: Exposure to contact center operations, the BPO industry, or managing Learning & Development (L&D) tools.
• Tool/Platform Experience: Prior experience managing L&D or tech-enabled training tools, especially AI or simulation platforms (e.g., SecondNature or similar).
• L&D/KPI Familiarity: Familiarity with KPI tracking methodologies and the basics of instructional design.

Job Type: Full-time

Benefits:

• Additional leave
• Company Christmas gift
• Company events
• Flextime
• Health insurance
• Opportunities for promotion
• Promotion to permanent employee
• Work from home

Work Location: Remote