Job Description

Role: AI Engineer Agent Development, MLOps & UI (GCP Vertex AI + LangChain)

Location: Atlanta, GA (Hybrid) - Need Locals or Nearby

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.

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