AI Architect

The role has the objective of harnessing AI and automation technologies to optimize and elevate eLearning development processes by creating scalable and efficient AI solutions. The role aims to enhance productivity, improve accuracy, and accelerate content creation in the eLearning development lifecycle. It also involves enhancing the learning experience by implementing AI techniques in learning modalities and learning platforms. The focus is on driving innovation to streamline workflows, reduce manual effort and thus bugs/errors, and deliver high-quality, engaging learning experiences.

Responsibilities:

• Lead AI strategy and define end-to-end architecture for eLearning and enterprise AI solutions, aligning with business objectives and transformation goals
• Collaborate with business, product, and technology stakeholders to identify use cases and translate requirements into scalable, enterprise-grade architectures
• Own overall system architecture across application, data, and AI layers, ensuring reusability, extensibility, and alignment with engineering standards
• Design secure AI systems leveraging LLMs (e. g., GPT models), including RAG pipelines and agentic workflows using frameworks such as LangChain or Semantic Kernel
• Define AI orchestration patterns covering prompt design, context management, multi-turn interactions, and tool/agent coordination
• Architect AI-driven capabilities, including automated content generation and multimodal experiences (text, audio, video, and image)
• Establish data architecture and governance frameworks, including data sourcing, quality, compliance, and scalable pipelines for structured and unstructured data
• Define cloud architecture and deployment strategy across Microsoft Azure, Amazon Web Services, or Google Cloud Platform, ensuring secure networking (VPC/VNet), private access, and scalable infrastructure
• Ensure solutions are secure, reliable, and cost-efficient by applying best practices in performance optimization, monitoring, and scaling
• Establish and enforce security, governance, and Responsible AI practices, ensuring compliance with standards such as GDPR, ISO 27001 and ISO/IEC 42001 and mitigating risks like prompt injection and data leakage
• Define and oversee MLOps/DevOps practices, including CI/CD, containerization, model lifecycle management, monitoring, and logging
• Architect integration strategies to embed AI capabilities into enterprise platforms (LMS/LXPSCORM, xAPI, LTI, CMS, CRM) via APIs and microservices
• Establish coding standards and API design guidelines, and conduct architecture and code reviews to ensure quality and consistency
• Collaborate with cross-functional teams across AI, engineering, product, and business to drive successful delivery

Requirements:

• Candidates should have 10+ years of experience in web application development using DevOps. 5+ years in AI system design with exposure to cloud-native architectures.
• Bachelor's/Master's in Computer Science, Engineering, or Data Science, or equivalent practical experience.
• 5+ years in AI system design with exposure to cloud-native architectures.
• Hands-on experience with LLMs (OpenAI, open-source models, etc. ), RAG pipelines and vector databases, and API-based AI integration.
• Experience with at least one major cloud platform: Amazon Web Services, Microsoft Azure, or Google Cloud Platform.
• Understanding of secure AI architecture (data isolation, private endpoints, IAM).

Key Skills:

• Proven experience designing and deploying ML and generative AI solutions into production with operational support.
• Strong expertise in LLMs (e. g., GPT models, LLaMA), including prompt engineering, evaluation, and optimization.
• Hands-on experience building agentic AI systems and RAG pipelines and working with vector databases using frameworks like LangChain or Semantic Kernel.
• Strong programming skills in Python with experience in designing and integrating RESTful APIs.
• Experience with cloud platforms such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform, including secure and scalable deployments.
• Solid understanding of data pipelines, data governance, and AI system architecture.
• Experience with MLOps practices, including model lifecycle management, CI/CD, monitoring, and performance optimization.
• Strong understanding of AI security and Responsible AI principles, including data privacy, compliance, and risk mitigation.
• AI: OpenAI API / Anthropic Claude API, prompt engineering, RAG, LangChain or similar orchestration framework, embeddings, and vector databases (Pinecone / Weaviate / pgvector).
• Backend: Python (FastAPI / Django) or Node.js, REST APIs, WebSockets for real-time chat, session/state management (AWS or Azure (hands-on, not just theoretical)), VPC, IAM, EC2/ECS/EKS, S3 RDS.
• DevOps: Docker, Kubernetes, CI/CD pipelines, GitHub Actions, or equivalent.
• Security: IAM design, secrets management, encryption, and basic AppSec awareness.
• Database: PostgreSQL or equivalent relational DB, plus a vector store for RAG.