AI/ML Model Development
• Build, train, and evaluate ML models, contributing to the full model lifecycle from data preparation through to deployment.
• Conduct exploratory data analysis, feature engineering, and model experimentation; document findings clearly.
• Support model validation, testing, and performance benchmarking activities.
Generative AI & LLM Applications
• Develop and maintain GenAI-powered applications including chatbots, summarization tools, document processing pipelines, and internal copilots.
• Implement prompt engineering patterns, retrieval-augmented generation (RAG) pipelines, and tool-augmented agents using established frameworks.
• Participate in the evaluation of new LLM capabilities and contribute to internal proof-of-concepts.
AI Infrastructure & MLOps
• Support the building and maintenance of ML pipelines, including data ingestion, preprocessing, training automation, and model serving.
• Manage and monitor deployed models; identify and escalate performance degradation, drift, or anomalies.
• Contribute to infrastructure-as-code for AI workloads across cloud environments (AWS, Azure, or GCP).
AI Governance & Quality
• Assist in producing governance documentation: model cards, data lineage records, and risk assessment inputs.
• Implement monitoring and logging frameworks to support auditability and compliance requirements.
• Apply responsible AI checklists and flag potential bias, fairness, or privacy concerns during development.
Collaboration & Delivery
• Work closely with data engineers, platform engineers, and business analysts to integrate AI outputs into existing systems.
• Participate in Agile ceremonies, sprint planning, and technical design discussions.
• Write clean, well-documented, testable code and maintain internal technical documentation.