GCP Vertex AI Engineer (RARR Job 5730)

We are looking for an experienced GCP Vertex AI Engineer with strong hands-on expertise in Generative AI, AI/ML, and Data Science. The ideal candidate will have a proven track record of building, deploying, and scaling production-grade GenAI and ML solutions using GCP Vertex AI and related cloud AI platforms.

ey Responsibilities:

• Design, develop, and deploy end-to-end Generative AI solutions using GCP Vertex AI and other cloud AI services.

• Build production-grade AI/ML applications, integrating Generative AI components such as LLMs, embeddings, retrieval pipelines, and fine-tuned models.

• Implement RAG (Retrieval-Augmented Generation) pipelines using structured and unstructured data.

• Develop GenAI applications using prompt engineering, embeddings, vector search, and fine-tuning techniques.

• Work with managed LLM services across GCP (preferred), Azure AI, and AWS Bedrock.

• Utilize GCP AI services such as Vertex AI, Vertex AI Vector Search, Gemini, Cloud Run, Cloud SQL, and other AI/ML data services.

• Work with agent frameworks such as Google ADK, LangGraph, CrewAI, etc.

• Write high-quality production code in Python, using frameworks like Flask and FastAPI for API development.

• Apply deep knowledge of AI/ML, deep learning, TensorFlow, NLP, and data pipelines.

• Collaborate with cross-functional teams to integrate AI capabilities into business applications.

• Ensure scalability, performance, and reliability of AI/ML solutions in production.

Required Skills & Experience:

• 5–16 years of hands-on experience in Generative AI, AI/ML, and Data Science.

• Strong expertise in GCP Vertex AI (preferred), with exposure to Azure AI or AWS Bedrock.

• Experience implementing RAG, LLM fine-tuning, embeddings, vector search, and GenAI workflows.

• Proficiency in handling structured and unstructured datasets.

• Solid understanding of AI/ML search, data services, and GCP ecosystem tools.

• Strong programming background in Python, including popular API frameworks (Flask, FastAPI).

• Experience with TensorFlow, NLP, Deep Learning, and related libraries.

• Strong understanding of Generative AI concepts, methodologies, and techniques.

Good to Have:

• Hands-on experience with agentic workflows and orchestration frameworks.

• Exposure to Gemini models and Google's latest AI stack.

• Familiarity with MLOps practices and production deployment workflows.