AI Engineer with GCP Vertex

AI Engineer with GCP Vertex Location: Atlanta ( 3 days in Office ) MUST BE eligible to work in the USA Employment Type: Contract (long term) Job Summary: We are seeking a hands-on AI Engineer to design, build, and deploy intelligent AI agents using GCP Vertex AI, LangChain, and modern UI tools like Streamlit. The ideal candidate will bring together skills in large language models (LLMs), agent orchestration, MLOps, and user-friendly interface development to create powerful and accessible AI solutions. Key Responsibilities: • Design and implement LLM-based agents using LangChain, integrated with GCP Vertex AI services. • Build interactive UIs using Streamlit or similar frameworks to showcase and test AI agent capabilities. • Develop end-to-end ML pipelines for training, evaluation, and deployment using tools like Vertex Pipelines, Kubeflow, or Airflow. • Integrate with APIs, vector databases, and knowledge sources to enable RAG (Retrieval-Augmented Generation)workflows. • Deploy scalable, secure AI services using CI/CD pipelines, infrastructure-as-code, and version-controlled model registries. • Monitor model performance, manage experiments, and optimize agent behavior in production environments. • Work cross-functionally with product, design, and engineering teams to deliver intuitive, high-impact AI-powered applications. Required Qualifications: • 3–6 years of hands-on experience in AI/ML engineering, including recent work with LLMs and LangChain. • Proficiency with GCP Vertex AI tools such as Pipelines, Model Registry, Training, and Endpoints. • Strong Python programming skills, with experience in FastAPI, Flask, or similar web frameworks. • Demonstrated experience building interactive dashboards or tools using Streamlit, Gradio, or Dash. • Knowledge of MLOps workflows, including tools like MLflow, Weights & Biases, or Vertex AI Experiments. • Experience working with vector stores (e.g., FAISS, Pinecone, Weaviate) in agent pipelines. • Familiarity with retrieval-based QA, embeddings, and prompt engineering techniques. • Experience with LangGraph or similar agent orchestration frameworks. Preferred Qualifications: • Familiarity with cloud-native deployment and DevOps tools (Terraform, Docker, GCP Cloud Build). • Background in UX/UI design thinking or rapid prototyping for AI-driven applications. • Experience integrating LLMs with external APIs or private knowledge sources.