Generative AI Engineer

What you'll do:

Develop and deploy end-to-end Gen AI 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. 

Optimize AI models for scalability, efficiency, and cost-effectiveness on GCP. 

Translate business requests into AI-enabled analytical and technical solutions. 

Translate business needs into technology requirements needed to create and deploy AI driven solutions. 

Provide technical guidance on AI across the organization, fostering a culture of innovation and collaboration. 

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'll need:

Bachelor’s degree or higher in Mathematics, Statistics, Data Science, Computer Science, Engineering, or a related quantitative field

7+ years in developing AI/ML solutions

1+ years experience with developing or deploying solutions with GenAI / LLMs

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

Expertise in Vertex AI, GCP AI/ML services (AutoML, BigQuery ML, Cloud Run, etc.) or a similar cloud technology

Proficiency in Python, LangChain, and Google AI APIs or similar

Experience with embeddings, vector databases and RAG (e.g. Google’s vector search)

Understanding of NLP, deep learning, and generative architectures (Transformers, Diffusion Models, etc.)

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

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.

We offer comprehensive compensation and healthcare packages, 401k matching, paid time off, and organizational growth potential through our online learning platform with guided career tracks.