Generative AI Engineer
On-siteAtlanta, Georgia, United States
Job Summary
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 into AI/LLM-based solutions using best practices, implement prompt engineering and RAG strategies to improve response accuracy, and integrate GenAI capabilities into data pipelines, dashboards, and APIs for real-time analytics. Understand how business requests translate into AI-enabled analytical solutions while maintaining a deep understanding of latest advancements in generative AI and its applications in data analysis. Communicate complex technical concepts clearly to both technical and non-technical audiences. Full-time role in Alpharetta or Atlanta, Georgia, 4 days/week (Mon-Thu). No immigration sponsorship available.
Required Qualifications
- 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
- Strong desire to contribute to cutting-edge projects
- Proficiency in Python
- Proficiency in LangChain
- Proficiency in 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
- This position requires being in either our Alpharetta or Atlanta, Georgia office 4 days/week on Mon - Thurs
Desired Qualifications
- Master's degree in a related field
- 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 of 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
- Financial industry experience
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