Generative AI Engineer

● Develop and deploy key components of GenAI solutions on Google Cloud Vertex AI to generate insights from structured and unstructured data. ● Integrate diverse data sources, data types, and data structures into AI / LLM based solutions, using data and technology best practices. ● Implement prompt engineering and RAG (Retrieval-Augmented Generation) strategies to improve response accuracy. ● Integrate GenAI capabilities into data pipelines and dashboards, and APIs for real-time analytics. ● Understand how business requests translate into AI-enabled analytical and technical solutions. ● Develop and maintain a deep understanding of the latest advancements in AI, particularly in generative AI and its applications in data analysis and insight generation. ● Communicate complex technical concepts clearly and concisely to both technical and non-technical audiences.

● Bachelor’s degree or higher in Computer Science or Computer Engineering, Statistics, Mathematics, or a related quantitative field ● 2+ years in shipping production grade software. ● 1+ years experience in deploying solutions on modern cloud environments. ● Ability to learn and apply new technologies; passion for staying abreast of the latest advancements in Generative AI research and technology; passion for AI research and a strong desire to contribute to cutting-edge projects ● Proficiency in Python, LangChain, and either Java or C/ C++ ● Experience with REST API clients and platforms such as Spring/ Flask. ● Strong communication skills of analytical results to technical and non-technical audiences alike

● Master’s degree in a related field is a strong plus ● Exposure in Vertex AI, GCP AI/ML services (AutoML, BigQuery ML, Cloud Run, etc.) or a similar cloud technology ● Strong foundational skills in Linux Operating System. ● Understanding of NLP, deep learning, and generative architectures (Transformers, Diffusion Models, etc.) ● Background in credit risk, financial data analytics or risk modeling. ● Experience working with large datasets on a big data platform (e.g., Google Cloud, AWS, Snowflake, Hadoop) ● Experience in Business Intelligence, data visualization, and customer insights generation. ● Familiarity with data governance, model bias mitigation, and regulatory frameworks (GDPR, AI Act, SEC compliance). ● Experience with MLOps practices, model monitoring, and CI/CD for AI workflows. ● Knowledge of prompt tuning, fine-tuning, and parameter-efficient methods (LoRA, PEFT). ● Hands-on experience with RAG, multi-modal AI, and hybrid AI architectures. ● Contributions to the AI community through publications, open-source projects, or conference presentations.