Agentic AI Architect
Auburn Hills, MI (Hybrid: 3 days onsite in a week)
6 months
Role:
- We are seeking a highly skilled Agentic AI Architect to lead the design and development of sophisticated AI agent systems using Google Cloud Platform's Vertex AI suite. This hands-on role requires deep expertise in building autonomous AI systems that can reason, plan, and execute complex tasks using large language models and advanced orchestration frameworks.
Key Responsibilities:
- Design and implement agentic AI architectures using Google Vertex AI models (Gemini, PaLM, Codey)
- Develop and deploy AI agents using LangChain and Model Context Protocol (MCP) architectures
- Build scalable, production-ready AI systems leveraging GCP services including Vertex AI Pipelines, Matching Engine, and AI Platform Implement multi-agent systems with complex reasoning, planning, and tool-use capabilities
- Design and optimize vector databases and embedding systems for retrieval-augmented generation (RAG)
- Integrate agents with external APIs, databases, and enterprise systems
- Establish MLOps practices for agent deployment, monitoring, and continuous improvement
Required Technical Skills:
Core AI & ML:
- Extensive experience with Google Vertex AI suite (Gemini Pro/Ultra, PaLM 2, Codey, Imagen)
- Deep understanding of agentic AI patterns: ReAct, Chain-of-Thought, Tree of Thoughts, and multi-agent workflows Proficiency with LangChain, LangGraph, and agent orchestration frameworks
- Experience with Model Context Protocol (MCP) for agent-to-agent communication
- Knowledge of prompt engineering, fine-tuning, and model optimization techniques
Google Cloud Platform:
- Vertex AI Workbench, Pipelines, and Model Registry Cloud Functions, Cloud Run, and App Engine for serverless deployments BigQuery for data processing and analytics Cloud Storage, Firestore, and Cloud SQL for data management Pub/Sub for event-driven architectures
Cloud Logging, Monitoring, and Error Reporting Programming & Development:
- Advanced Python programming (5+ years) Experience with FastAPI, Flask, or Django for API development Containerization with Docker and Kubernetes (GKE) Infrastructure as Code (Terraform, Cloud Deployment Manager) Version control with Git and CI/CD pipelines (Cloud Build, GitHub Actions)
Data & Vector Systems:
- Vector databases (Vertex AI Matching Engine, Pinecone, Weaviate, or Chroma) Embedding models and similarity search optimization
- Knowledge graph construction and reasoning
- Structured and unstructured data processing
Preferred Qualifications:
- Experience with additional agent frameworks (AutoGen, CrewAI, Semantic Kernel)
- Knowledge of graph databases (Neo4j, Amazon Neptune)
- Familiarity with other cloud platforms (AWS Bedrock, Azure OpenAI)
- Understanding of function calling, tool use, and external API integration
- Experience with streaming and real-time AI applications
- Background in distributed systems and microservices architecture
- Knowledge of AI safety, alignment, and responsible AI practices.
- Experience on Agile methodologies
- Solid understanding of core and modern technologies around Cloud, APIs, Web-services
Experience Requirements
- 10+ years in software engineering with 3+ years focused on AI/ML systems
- 2+ years hands-on experience with production LLM applications
- Demonstrated experience building and deploying agentic AI systems
- Track record of architecting scalable cloud-native applications
- Experience leading technical teams and mentoring junior developers
- Experience working in automotive
Education:
- Bachelor's or Master's degree in Computer Science, AI/ML, or related technical field
- Preferred Relevant certifications (Google Cloud Professional ML Engineer, AI/ML specializations) preferred
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Spectraforce Technologies
Jun 23, 2025