Generative AI Engineer

What you'll do

● 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.

What experience you need:

● 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

What could set you apart:

● 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