AI Associate Engineer

- Bachelors degree in AI Engineering, Computer Science, Data Science, or a related technical discipline, or equivalent combination of education and experience. - 1-2 years of experience in developing AI solutions, including AI agents and chatbots, leveraging industry-leading platforms such as Azure AI Foundry. - Proficient in delivering solutions using Python, SQL, and NoSQL technologies. - Experience in both front-end and back-end development with frameworks such as React, Angular, Node.js, Django, or Flask for creating interfaces and services. - Skilled in API development and integration, specifically in building RESTful APIs to connect AI models to applications. - Familiarity with Large Language Models (LLMs), prompt engineering, and fine-tuning techniques. - Expertise in Natural Language Processing (NLP), including tokenization and conversational AI methods. - Experience utilizing Retrieval-Augmented Generation (RAG) for grounding models in accurate data and minimizing inaccuracies. - Competence in building data pipelines and preprocessing both structured and unstructured data using tools like Databricks or similar technologies. - Familiar with Vector and Graph Databases for semantic search and recommendation systems. - Knowledge of implementing Model Governance to ensure transparency, bias detection, and compliance. - Understanding of security protocols to ensure the safe deployment of AI models and protection of sensitive information. - Strong grasp of CI/CD processes, Git, and Agile methodologies. - Familiarity with AI-assisted coding tools like GitHub Copilot within development workflows. - Excellent documentation, communication, and presentation skills to articulate designs and solutions to both business and technical stakeholders. - Solid understanding of software engineering principles, including data structures, debugging, unit testing, and producing maintainable, well-documented code. - Awareness of dependency management, logging, error handling, and differences between development, testing, and production environments. - Experience in assessing AI and GenAI outputs, applying secure data-handling practices while handling sensitive data. - Ability to work from specified technical requirements and implementation plans while incorporating feedback from code reviews and effectively communicating progress and considerations within cross-functional teams.