AI Architect

The Agentic AI Architect will design, develop, and implement autonomous AI agents using LLMs (Large Language Models), GenAI, and multi-agent systems within the GCP ecosystem. This role requires expertise in Vertex AI, Generative AI Studio, BigQuery ML, and GCP AI/ML services, along with deep knowledge of agentic AI frameworks such as LangChain, AutoGen, and CrewAI.
Key Responsibilities
Architect and design agentic AI systems that leverage LLMs, RAG (Retrieval-Augmented Generation), and multi-agent collaboration frameworks.
Develop intelligent, autonomous AI agents that interact with structured and unstructured data, using Vertex AI and GCP AI services.
Optimize vector search and embeddings using GCPs Vertex AI Matching Engine (FAISS-based) and hybrid search techniques.
Implement event-driven and API-based workflows for AI automation using Google Cloud Functions, Cloud Run, and Pub/Sub.
Design MLOps and LLMOps pipelines for model training, deployment, monitoring, and continuous fine-tuning.
Ensure AI governance, compliance, and security best practices in multi-agent AI deployments.
Collaborate with data engineers, ML engineers, and solution architects to build scalable AI applications.
Drive the integration of AI agents with enterprise systems, including BigQuery, Looker, Apigee, and Document AI.
Optimize performance of LLMs on GCP TPU/GPU instances for efficient model inference.
Required Skills & Experience:
10+ years in AI, ML, or software architecture, with a strong focus on agentic AI.
Strong expertise in GCP AI/ML ecosystem (Vertex AI, AutoML, Generative AI Studio, BigQuery ML).
Experience with LangChain, AutoGen, CrewAI, OpenAI API, and similar AI frameworks.
Proficiency in Python, PyTorch, TensorFlow, or JAX for AI model development.
Strong knowledge of vector databases (Vertex AI Matching Engine, Pinecone, Weaviate, ChromaDB).
Hands-on experience with Google Cloud Functions, Cloud Run, Cloud Composer (Airflow), and Dataflow.
Experience with RAG (Retrieval-Augmented Generation) for knowledge retrieval optimization.
Strong understanding of AI security, governance, and ethical AI principles.
Proven ability to design, scale, and optimize AI-driven applications in production.
Preferred Qualifications:
Hands-on experience with Googles Generative AI offerings (PaLM 2, Gemini, Vertex AI Pipelines).
Experience integrating multi-modal AI models in GCP (text, vision, speech).
Knowledge of AI observability tools (Vertex AI Model Monitoring, Explainable AI).
Experience in real-time streaming AI applications using Pub/Sub and Dataflow.