Lead AI Developer
7 days ago
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.
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