Machine Learning Engineer

• Development of machine learning models.
• Data preparation and preprocessing.
• Training, validation and optimization of models.
• Deployment of models to production environments.
• Performance monitoring and continuous improvement.
• Collaboration with Data and business teams.

AI Engineer

• Model Development – Design, train, fine-tune, and evaluate models spanning classical ML, deep learning (CNNs, transformers), and generative AI (LLMs, diffusion).
• Data Exploration & Analytics – Conduct exploratory data analysis, statistical testing, and time-series / forecasting to inform features, prompts, and business KPIs.
• End-to-End Pipelines – Build reproducible workflows for data ingestion, feature engineering / prompt stores, training, CI/CD, and automated monitoring.
• LLM & Agentic AI Engineering – Craft prompts, retrieval-augmented generation (RAG) pipelines, and autonomous/assistive agents; fine-tune LLMs on domain-specific datasets to boost accuracy and align outputs with product requirements.
• AI Automation & Integration – Expose AI components as micro-services and event-driven workflows; integrate with orchestration tools (Airflow, Prefect) and business APIs to automate decision pipelines.
• Continuous Learning – Track advances in LLMs, vision, and analytics; share insights and best practices with the wider engineering team.
• Mentor junior engineers and contribute to technical direction and engineering best practices.

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Machine Learning Engineer

Senior AI Engineer