Synechron
Ai Architect
Overview
We are seeking an experienced AI Architect to define, govern, and drive enterprise AI architecture initiatives across large-scale AI ecosystems, platforms, and transformational programs.
About Synechron
At Synechron, we harness the power of digital transformation to drive business success. As a global consulting firm, we combine creativity with advanced technology to deliver innovative solutions across industries.
Requirements & Eligibility
- 8+ years of experience in Enterprise Architecture, Solution Architecture, AI Architecture, Cloud Architecture, Software Engineering, or related disciplines.
- Proven experience designing and delivering large-scale enterprise technology or transformation initiatives.
- Strong expertise in Generative AI, LLMs, RAG, AI agents, multi-agent systems, prompt engineering, and modern AI architectures.
- Hands-on experience designing enterprise AI platforms and cloud-native solutions on Azure.
- Strong knowledge of Azure AI services, APIs, microservices, distributed systems, integrations, and modern architecture patterns.
- Experience with MLOps, observability, security, resilience, and operational best practices.
- Understanding of Responsible AI, governance, cybersecurity, privacy, compliance, and risk management principles.
- Experience leading architectural decisions and collaborating with senior business and technology stakeholders.
Key Responsibilities
- Define and drive enterprise architecture for AI, Generative AI, RAG, AI agents, and multi-agent ecosystems.
- Establish AI platform strategies, technology roadmaps, architectural standards, and governance frameworks.
- Design scalable, secure, and cloud-native AI solutions leveraging Azure AI services and modern architecture patterns.
- Translate business requirements into solution architectures and implementation roadmaps.
- Guide engineering teams on architecture decisions, performance optimization, technical risks, and production readiness.
- Review solution designs, proofs of concept, integrations, data architectures, and MLOps practices to ensure alignment with enterprise standards.
- Drive adoption of AI best practices, including LLM architecture, prompt engineering, orchestration frameworks, and Responsible AI principles.
- Collaborate with engineering, DevOps, security, and business teams to ensure successful delivery of enterprise AI solutions.
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