IVR Systems Operations Engineer Sr

IVR Systems Operations Engineer Sr

Required Qualifications

• Extensive Industry Experience: Proven experience (e.g. 5+ years) in software development and architecture, building large-scale AI or cloud-based solutions in production environments.
• AI & Voice Expertise: Hands-on expertise with AI/ML technologies, including natural language processing (NLP) or speech recognition. Familiarity with large language models (e.g., GPT, Claude or similar) and conversational AI/voice assistant development.
• Microsoft Azure and AWS Proficiency: Strong experience developing with Microsoft Azure and AWS cloud services. Knowledge of CI/CD, Azure and AWS container services for deploying AI workloads.
• Programming & DevOps Skills: Proficient in modern programming languages (such as Python, Java, C#, or similar) and building APIs or microservices. Solid understanding of DevOps and CI/CD practices (e.g., Azure DevOps pipelines) to ensure frequent, reliable delivery of AI software.
• System Design & Architecture: Deep knowledge of scalable, distributed systems design and cloud-native architecture. Experience implementing microservices, containerization, and API integration.
• Problem Solving & Innovation: Strong analytical and problem-solving skills with the ability to navigate complex technical challenges. Innovative mindset to drive continuous improvement and to evaluate emerging technologies for potential adoption.
• Communication: Excellent communication skills, with ability to explain complex AI solutions and technical strategies to diverse stakeholders.

Preferred Qualifications

• Financial Services Familiarity: Experience working in banking, fintech, or other heavily regulated industries and understanding of compliance, security, and data privacy considerations for AI solutions in these environments.
• AI Research & Innovation: Demonstrated interest in emerging AI trends (such as agentic AI, multi-modal models, reinforcement learning, etc.) and experience bringing new AI concepts from prototype to production.
• Advanced Degree: Master’s or higher degree in Computer Science, Engineering, AI, or related field (or equivalent practical experience) is a plus.

Required Qualifications

• Extensive Industry Experience: Proven experience (e.g. 5+ years) in software development and architecture, building large-scale AI or cloud-based solutions in production environments.
• AI & Voice Expertise: Hands-on expertise with AI/ML technologies, including natural language processing (NLP) or speech recognition. Familiarity with large language models (e.g., GPT, Claude or similar) and conversational AI/voice assistant development.
• Microsoft Azure and AWS Proficiency: Strong experience developing with Microsoft Azure and AWS cloud services. Knowledge of CI/CD, Azure and AWS container services for deploying AI workloads.
• Programming & DevOps Skills: Proficient in modern programming languages (such as Python, Java, C#, or similar) and building APIs or microservices. Solid understanding of DevOps and CI/CD practices (e.g., Azure DevOps pipelines) to ensure frequent, reliable delivery of AI software.
• System Design & Architecture: Deep knowledge of scalable, distributed systems design and cloud-native architecture. Experience implementing microservices, containerization, and API integration.
• Problem Solving & Innovation: Strong analytical and problem-solving skills with the ability to navigate complex technical challenges. Innovative mindset to drive continuous improvement and to evaluate emerging technologies for potential adoption.
• Communication: Excellent communication skills, with ability to explain complex AI solutions and technical strategies to diverse stakeholders.

Preferred Qualifications

• Financial Services Familiarity: Experience working in banking, fintech, or other heavily regulated industries and understanding of compliance, security, and data privacy considerations for AI solutions in these environments.
• AI Research & Innovation: Demonstrated interest in emerging AI trends (such as agentic AI, multi-modal models, reinforcement learning, etc.) and experience bringing new AI concepts from prototype to production.
• Advanced Degree: Master’s or higher degree in Computer Science, Engineering, AI, or related field (or equivalent practical experience) is a plus.