Generative AI Engineer

About the Company We are looking for a skilled AI Engineer with deep expertise in Google Cloud Platform (GCP) to design, build, and deploy production-grade machine learning and generative AI solutions. You will be responsible for taking AI models from experimental prototypes to scalable, high-availability enterprise services using GCP’s native AI/ML ecosystem. About the Role We are looking for a skilled AI Engineer with deep expertise in Google Cloud Platform (GCP) to design, build, and deploy production-grade machine learning and generative AI solutions. Responsibilities * Model Deployment \& Pipeline Architecture: Design, build, and maintain end-to-end ML and LLM pipelines using Vertex AI, Kubeflow, and Dataflow. * Generative AI \& LLM Integration: Fine-tune, evaluate, and integrate foundation models (e.g., Gemini) via Vertex AI Model Garden into application workflows using RAG architecture. * MLOps \& Infrastructure: Automate continuous integration, deployment, and monitoring (CI/CD/CT) for machine learning models using GCP infrastructure and Terraform. * Data Engineering Collaboration: Work with data teams to optimize feature stores, data pipelines (BigQuery, Pub/Sub), and training datasets for scalable AI workflows. * Performance \& Cost Optimization: Monitor model drift, latency, and throughput in production while optimizing GCP resource utilization and infrastructure costs. Qualifications * Technical Skills * GCP AI Ecosystem: Hands-on experience with Vertex AI (Pipelines, Feature Store, Model Registry, Endpoint deployment), BigQuery ML, and Cloud Run/GKE. * Frameworks \& Languages: Proficiency in Python and standard ML frameworks (PyTorch, TensorFlow, JAX). * Generative AI Stack: Experience with orchestration frameworks (LangChain, LlamaIndex), vector databases (Vertex AI Vector Search, Pinecone, or pgvector), and prompt engineering/tuning. * MLOps Tools: Familiarity with Docker, Kubernetes, Terraform, MLflow, or Kubeflow Pipelines. Experience \& Background * 3+ years of professional experience in software engineering, machine learning engineering, or data science. * Proven track record of deploying and maintaining ML/AI models in a cloud-native production environment (preferably GCP). * Bachelor’s or Master’s degree in Computer Science, Data Science, Electrical Engineering, or a related quantitative field (or equivalent practical experience). Preferred Skills * Google Cloud Certified - Professional Machine Learning Engineer or Professional Cloud Architect. * Experience with serverless AI applications and event-driven architectures on GCP.