AI Engineer

Role: AI Engineer

Duration: 6+ months

Location: Bay Area, CA (hybrid)

Technology: Exp in Google Cloud Platform (Gemini, Vertex) 

Responsibilities:

• Design and implement end-to-end ML pipelines on Google Cloud Platform (Google Cloud Platform)
• Build, fine-tune, and optimize AI/ML models for production deployment using Vertex AI and Gemini models
• Develop Generative AI solutions leveraging Gemini APIs, prompt engineering, Retrieval-Augmented Generation (RAG), and multimodal AI capabilities
• Develop and maintain RESTful APIs for ML and GenAI model serving using FastAPI/Flask
• Implement vector search and semantic retrieval capabilities using BigQuery ML, Vertex AI Matching Engine, and embeddings
• Create automated testing, validation, CI/CD, and deployment pipelines for ML/AI workflows
• Set up model monitoring, observability, drift detection, and performance tracking for production AI systems
• Optimize model inference, scalability, latency, and serving infrastructure on Google Cloud Platform
• Collaborate with data engineering, product, and business teams to deliver scalable AI-driven applications
• Work with containerized deployments using Docker and Google Kubernetes Engine (GKE)

Required Skills

• Must have minimum 4+ years of relevant experience 
• Strong Python programming skills with ML frameworks such as PyTorch and TensorFlow
• Hands-on experience with Large Language Models (LLMs), Generative AI, and prompt engineering
• Experience working with Google Gemini models and Gemini APIs for GenAI use cases
• Strong proficiency in Google Cloud Platform AI services including Vertex AI, Gemini, Cloud ML Engine, BigQuery ML, and Vertex AI Matching Engine
• Experience implementing vector databases/search, embeddings, semantic search, and RAG architectures
• Expertise in RESTful API development using FastAPI or Flask
• Experience with Docker, Kubernetes, and Google Kubernetes Engine (GKE)
• Strong understanding of CI/CD pipelines and MLOps workflows for ML/AI deployments
• Experience with model monitoring, performance optimization, and scalable AI serving architecture