Artificial Intelligence Engineer ( GCP Vertex AI )

Role & responsibilities

Job Summary:

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:

• Relevant of 36 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.