AES - DE - ZenseAI Integration Specialist

The AI Engineer will be responsible for integrating ZenseAI capabilities into client systems, applications, websites, APIs, and business workflows. The role involves requirement analysis, system design, development, testing, deployment, monitoring, troubleshooting, and continuous improvement of AI-driven solutions.

A. Requirement Analysis & Solution Design

• Gather and analyse client requirements for AI use cases.
• Evaluate client architecture and identify integration points.
• Design scalable AI integration solutions using ZenseAI.
• Define technical specifications and integration approach.
• Create solution architecture documentation and technical designs.
• Collaborate with business stakeholders, architects, and development teams.

B. ZenseAI Integration

• Integrate ZenseAI services with client applications.
• Develop and maintain APIs, SDK integrations, and middleware components.
• Configure authentication, authorization, and secure data exchange mechanisms.
• Implement AI workflows, prompts, automation flows, and business rules.
• Ensure seamless communication between client systems and ZenseAI services.
• Support integration with CRM, ERP, CMS, ServiceNow, SharePoint, Teams, or other enterprise platforms.

C. Development & Customisation

• Build custom connectors and integration components.
• Develop reusable modules for AI interactions.
• Customize AI responses and business workflows based on client requirements.
• Implement data preprocessing and validation mechanisms.
• Extend platform capabilities when standard functionality is insufficient.

D. Testing & Quality Assurance

• Create integration test plans and test cases.
• Perform unit, integration, system, and user acceptance testing.
• Validate AI response accuracy and business rule compliance.
• Conduct performance and scalability testing.
• Ensure production readiness before deployment.

E. Production Support & Issue Resolution

• Investigate and resolve ZenseAI integration issues.
• Troubleshoot API failures, latency, authentication, and connectivity problems.
• Analyse logs and system metrics to identify root causes.
• Provide timely resolution to incidents and service requests.
• Coordinate with ZenseAI product teams and client stakeholders during critical issues.
• Perform root cause analysis (RCA) and implement preventive measures.

F. Monitoring & Optimisation

• Monitor system health and AI service performance.
• Track usage metrics, response time, and reliability.
• Optimise prompts, workflows, and integrations.
• Recommend improvements to increase efficiency and user experience.
• Ensure SLA compliance and service availability.

G. Security & Compliance

• Implement security best practices for AI integrations.
• Ensure compliance with client security policies and regulations.
• Manage access controls and data protection mechanisms.
• Conduct security reviews and vulnerability assessments.
• Support governance and audit requirements.

H. Documentation & Knowledge Management

• Create technical documentation and implementation guides.
• Maintain runbooks and support procedures.
• Document architecture, APIs, configurations, and workflows.
• Deliver knowledge transfer sessions to support teams.
• Maintain issue and resolution repositories.
• 3 to 8+ years in software engineering and integrations.
• Experience with AI/LLM platforms preferred.
• Experience with enterprise application integrations and production support.

A. Requirement Analysis & Solution Design

• Gather and analyse client requirements for AI use cases.
• Evaluate client architecture and identify integration points.
• Design scalable AI integration solutions using ZenseAI.
• Define technical specifications and integration approach.
• Create solution architecture documentation and technical designs.
• Collaborate with business stakeholders, architects, and development teams.

B. ZenseAI Integration

• Integrate ZenseAI services with client applications.
• Develop and maintain APIs, SDK integrations, and middleware components.
• Configure authentication, authorization, and secure data exchange mechanisms.
• Implement AI workflows, prompts, automation flows, and business rules.
• Ensure seamless communication between client systems and ZenseAI services.
• Support integration with CRM, ERP, CMS, ServiceNow, SharePoint, Teams, or other enterprise platforms.

C. Development & Customisation

• Build custom connectors and integration components.
• Develop reusable modules for AI interactions.
• Customize AI responses and business workflows based on client requirements.
• Implement data preprocessing and validation mechanisms.
• Extend platform capabilities when standard functionality is insufficient.

D. Testing & Quality Assurance

• Create integration test plans and test cases.
• Perform unit, integration, system, and user acceptance testing.
• Validate AI response accuracy and business rule compliance.
• Conduct performance and scalability testing.
• Ensure production readiness before deployment.

E. Production Support & Issue Resolution

• Investigate and resolve ZenseAI integration issues.
• Troubleshoot API failures, latency, authentication, and connectivity problems.
• Analyse logs and system metrics to identify root causes.
• Provide timely resolution to incidents and service requests.
• Coordinate with ZenseAI product teams and client stakeholders during critical issues.
• Perform root cause analysis (RCA) and implement preventive measures.

F. Monitoring & Optimisation

• Monitor system health and AI service performance.
• Track usage metrics, response time, and reliability.
• Optimise prompts, workflows, and integrations.
• Recommend improvements to increase efficiency and user experience.
• Ensure SLA compliance and service availability.

G. Security & Compliance

• Implement security best practices for AI integrations.
• Ensure compliance with client security policies and regulations.
• Manage access controls and data protection mechanisms.
• Conduct security reviews and vulnerability assessments.
• Support governance and audit requirements.

H. Documentation & Knowledge Management

• Create technical documentation and implementation guides.
• Maintain runbooks and support procedures.
• Document architecture, APIs, configurations, and workflows.
• Deliver knowledge transfer sessions to support teams.
• Maintain issue and resolution repositories.

1. Technical Skills Required

AI & Integration Technologies

• AI/ML fundamentals
• Generative AI platforms
• Large Language Models (LLMs)
• Prompt Engineering
• AI Workflow Automation
• RAG (Retrieval-Augmented Generation)

Development Skills

• Python
• JavaScript / TypeScript
• Node.js
• REST APIs
• GraphQL
• Microservices Architecture

Cloud & DevOps

• AWS / Azure / GCP
• Docker
• Kubernetes
• CI/CD Pipelines
• GitHub / GitLab

Data Technologies

• SQL Databases
• PostgreSQL
• MongoDB
• Vector Databases
• Data Modelling

Monitoring & Support

• Application Monitoring Tools
• Logging & Alerting Frameworks
• Incident Management
• Root Cause Analysis
• Performance Tuning