AI Platform Architect: Agentic & Generative AI - Cogentiq

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.

Responsibilities

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, RAG pipelines, vector databases, and LLM integrations; Define modular, microservices-based, API-first architectures enabling rapid development of AI copilots and autonomous systems; Define architectural patterns for agent orchestration, planning, reflection, reasoning loops, human-in-the-loop and feedback-driven systems; Evaluate and integrate emerging frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI into enterprise platforms; Ensure production-grade reliability including observability, monitoring, evaluation, guardrails, safety, governance, and cost optimization for LLM-based systems; Lead architecture across LLMOps, MLOps, and platform operations; Drive cloud-native platform architecture using AWS, Azure, or GCP; Lead Kubernetes-based deployments, CI/CD pipelines, and Infrastructure as Code; Ensure scalability, security, and resilience of AI workloads; 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

Requirements

12–15 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

Nice 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