Lead AI Developer

Role & responsibilities

• Understand workflows/processes and devise strategies for the areas applicable to AI-based implementation.
• Translate eLearning requirements into AI-driven automation strategies.
• Define data requirements and provide datasets for AI model training specific to eLearning.
• Design and maintain data pipelines (Azure Data Factory, Databricks, Synapse Analytics) to ensure clean, structured content for training.
• Apply governance frameworks (Azure Policy, Purview, DLP) to ensure responsible AI practices.
• Ensure fairness and transparency in AI models using Fair learn and Interpret ML.
• Develop and optimize AI models for automating eLearning processes, including interactive storyboards, content creation/development, and adaptive/customised learning paths.
• Leverage Azure Cognitive Services, Machine Learning Studio, and Azure OpenAI for scalable eLearning solutions.
• Use Adobe/other AI tools for automating audio narration, translation, video editing, and image creation for eLearning modules.
• Implement AI-driven workflows for converting text-based content into engaging eLearning assets.
• Design guardrails for ethical AI use in eLearning, ensuring compliance with GDPR and ISO 27001.
• Implement secure environments with Azure Key Vault, Security Center, and RBAC.
• Deploy AI models using Azure Machine Learning Pipelines and monitor performance.
• Set up CI/CD workflows for continuous optimization and scaling of AI solutions.
• Integrate AI into LMS and CMS primarily through APIs and custom integrations (with an extensibility consideration for integrating it with other platforms like Project Management Tool, Dashboards, etc.)
• Build AI-powered plugins for automating course creation, learner analytics, and feedback.
• Document AI workflows and provide training, architecture diagrams, and code snippets for team members.
  

Preferred candidate profile

• At least 5-8 years of experience deploying Machine Learning and Generative AI models into production with operational support.
• Advanced knowledge of Machine Learning and LLMs (e.g., GPT, Llama), with experience in LangChain, RAG, and model tuning.
• Proficient in MLOps tools (e.g., ML flow) for lifecycle management of machine learning models.
• Strong programming skills in Python, JavaScript, and RESTful APIs, with clean, efficient coding for AI Assistant and platform integrations.
• Experience with cloud computing platforms (AWS, Google Cloud, Azure) and cloud-based application deployment for model scaling.
• Deep knowledge of AI, NLP, and Conversational AI platforms (e.g., Bot Framework, PVA, Cognigy).
• Proficient in Azure AI Suite, including Cognitive Services, Machine Learning Studio, and other tools.
• Experience with Fairlearn, InterpretML, and adherence to GDPR, HIPAA, and ISO 27001 standards.
• Exposure to multimedia automation tools (e.g., Adobe AI tools) for video/image editing workflows.
• Exposure to eLearning development tools, and processes.