AI Platform Architect Agentic & Generative AI Cogentiq

Role Overview

As an AI Platform Architect you will design and scale enterprise-grade Agentic AI and Generative AI platforms that power copilots autonomous workflows and multi-agent systems. This is a hands-on architect role for a builder who combines deep distributed-systems thinking with practical expertise in LLMs agent orchestration and cloud-native platforms.

You will define reference architectures engineering standards and core platform capabilities that enable teams to build deploy and operate AI systems at scale.

Key Responsibilities

Platform & Architecture Leadership

• Architect scalable Agentic AI platforms supporting multi-agent orchestration tool usage memory planning and reasoning workflows
• Design end-to-end GenAI platforms including:
  • Prompt orchestration
  • Retrieval-Augmented Generation (RAG) pipelines
  • Vector databases
  • LLM integrations
• Define modular microservices-based API-first architectures enabling rapid development of AI copilots and autonomous systems

Agentic & GenAI Systems

• Define architectural patterns for:
  • Agent orchestration and coordination
  • Planning reflection and reasoning loops
  • Human-in-the-loop and feedback-driven systems
• Evaluate and integrate emerging frameworks such as LangChain LlamaIndex AutoGen CrewAI and related ecosystems into enterprise platforms

Production Readiness & Reliability

• Ensure production-grade reliability across AI platforms including:
  • Observability and monitoring
  • Evaluation and experimentation frameworks
  • Guardrails safety and governance
  • Cost optimization for LLM-based systems
• Lead architecture across LLMOps MLOps and platform operations

Cloud DevOps & Infrastructure

• Drive cloud-native platform architecture using AWS Azure or GCP
• Lead Kubernetes-based deployments CI/CD pipelines and Infrastructure as Code (IaC)
• Ensure scalability security and resilience of AI workloads

Collaboration & Technical Leadership

• Partner with data science product and business teams to translate AI use cases into robust platform capabilities
• Mentor senior engineers and architects; set engineering standards reference architectures and best practices
• Act as a technical thought leader for enterprise AI platform strategy

Required Skills & Experience

• 1215 years of experience in platform engineering / architecture with strong depth in distributed systems
• Deep expertise in cloud platforms (AWS / Azure / GCP) and Kubernetes-based architectures
• Hands-on experience with LLMs RAG architectures and vector databases (e.g. Pinecone FAISS Weaviate)
• Strong understanding of agentic frameworks multi-agent systems and orchestration patterns
• Experience across LLMOps / MLOps including:
  • Model deployment
  • Monitoring and evaluation
  • Lifecycle and version management
• Proficiency in microservices APIs event-driven systems and scalable backend engineering
• Strong coding experience in Python / Java / Go with system-design depth
• Experience working with data platforms (real-time batch large-scale data processing)
• Solid understanding of AI safety governance and responsible AI frameworks

Good to Have

• Experience building enterprise AI copilots or autonomous workflows
• Exposure to reasoning frameworks chain-of-thought orchestration and agent memory systems
• Prior consulting or client-facing experience leading AI-driven transformations

What Were Looking For

A builderarchitect who combines deep engineering rigor with a strong grasp of Agentic and Generative AIsomeone who can turn cutting-edge AI concepts into scalable secure and production-ready enterprise platforms.