Technical Architect - ML

Quantiphi is an AI-first digital engineering and consulting company delivering cloud, data engineering, and artificial intelligence solutions. The company is seeking a Technical Architect - ML to architect, build, deploy, and maintain production-grade agentic AI systems and multi-agent workflows, including RAG, LLM integration, cloud deployment, monitoring, and evaluation frameworks.

Responsibilities:

• Architect & Build Agentic Systems: Design and develop end-to-end multi-agent systems from scratch. You will create the foundational agent harnesses, define communication protocols, and build orchestration layers using frameworks like CrewAI, Langgraph, and AutoGen. Architectural decisions to ensure:

○ Hierarchical and collaborative multi-agent structures with well-defined agent roles, responsibilities, and communication protocols

○ Dynamic task decomposition, sophisticated tool integration, planning mechanisms (ReAct), and self-correction loops

○ Develop state management systems and memory mechanisms for persistent agent interactions

• Engineer Advanced Agent Capabilities: Develop custom agent-tools and define specialized agent-skills that empower agents to perform complex, domain-specific tasks
• Pioneer Context Engineering: Implement advanced context engineering and memory systems to ensure agents maintain state, learn from interactions, and make informed decisions in dynamic environments
• Deploy Production-Grade Solutions: Own the deployment, scaling, and maintenance of robust, low-latency agentic systems on major cloud platforms (GCP, AWS, or Azure). You will implement best-in-class MLOps practices for monitoring, continuous integration/continuous deployment (CI/CD), and system reliability
• Integrate and Optimize LLMs: Integrate LLMs to serve as the core reasoning engines for autonomous agents. You will apply advanced techniques like RAG and PEFT to optimize performance
• Create and maintain comprehensive tool libraries for agents including API integrations, database queries, and external service connections
• Design and implement RAG systems using vector databases (Pinecone, Weaviate, ChromaDB)
• Develop custom tools and plugins that enable agents to interact with various enterprise systems and APIs
• Ensure tool reliability, error handling, and seamless integration within agentic workflows
• Implement comprehensive monitoring and tracing systems for agent behavior, performance, cost optimization, and latency analysis
• Design novel evaluation frameworks to assess multi-step agentic task success, reliability, and accuracy
• Utilize advanced observability tools (LangSmith, Arize AI, or custom solutions) to trace agent decision making processes
• Establish metrics and KPIs for measuring agentic system performance in production environments