Generative AI Engineer

Job Description

We are seeking a Generative AI Engineer focused on building and deploying production-grade AI solutions on Google Cloud Platform (GCP) and Vertex AI. The engineer will develop LLM applications, implement RAG architectures, integrate structured and unstructured data, and deliver AI-powered insights for business users. This role bridges Software Engineering, Machine Learning Engineering, and Generative AI, with a strong emphasis on cloud-native development and production deployment.

Responsibilities

• Build and deploy Generative AI solutions using Google Vertex AI.
• Develop LLM pipelines that generate business insights from large datasets.
• Design and implement RAG (Retrieval-Augmented Generation) solutions.
• Perform prompt engineering and model optimization.
• Integrate AI capabilities into APIs, dashboards, analytics platforms, and data pipelines.
• Work with structured and unstructured data sources.
• Collaborate with business stakeholders to translate requirements into AI solutions.
• Stay current with advancements in LLMs, NLP, and Generative AI technologies.
• Communicate technical concepts to both technical and non-technical audiences.

Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, Mathematics, Statistics, or related quantitative discipline
• 2+ years of software engineering experience.
• 1+ year deploying solutions in cloud environments.
• Strong programming skills in Python (Flask), LangChain, Java (Spring) or C/C++
• Cloud-based application deployment
• Strong communication and stakeholder engagement skills.

Nice-to-have:

• Master’s degree in a technical field
• Hands-on experience with Google Cloud AI technologies, including Vertex AI, GCP, BigQuery ML, Cloud Run, and AutoML
• Knowledge of AI/ML concepts, including NLP, Transformers, Deep Learning, and Diffusion Models
• Experience developing advanced GenAI solutions using RAG, Multi-modal AI, Fine-tuning, LoRA, and PEFT
• Familiarity with MLOps, model monitoring, and CI/CD pipelines for AI applications
• Experience working with large-scale data platforms such as Snowflake, Hadoop, AWS, or other Big Data environments
• Background in financial services, credit risk, or risk analytics
• Understanding of AI governance, regulatory compliance, GDPR, AI Act, and SEC requirements