AI Architect

Enterprise AI | Generative AI | Agentic AI | Cloud Architecture | Hyderabad

Generative AI and agentic systems are moving from pilots to the core of how enterprises operate — and Tachyon Technologies is where that shift is being architected. Were looking for a senior AI Architect who wants to own that transformation end to end: designing the systems, setting the standards, and seeing solutions run in production at enterprise scale, not just in a slide deck.

This is a role for someone who has already done real cloud architecture, has since built genuine depth in LLMs, RAG, and agentic design, and wants a platform where that combination is the whole job — not a side project bolted onto something else.

• Architect end-to-end enterprise AI solutions spanning applications, APIs, data, cloud infrastructure, security, integrations, and user experience.
• Design and lead Generative AI and Agentic AI solutions using LLMs, RAG, enterprise search, tool/function calling, and multi-agent orchestration.
• Define cloud-native architectures for AI workloads on Azure, AWS, and/or GCP using managed AI services, containers, Kubernetes, and serverless patterns.
• Evaluate and select models, frameworks, vector databases, and cloud services against business, security, performance, and cost requirements.
• Design integrations between AI solutions and enterprise platforms — Salesforce, SAP, ServiceNow, ERP/CRM, data platforms, and external APIs.
• Create architecture blueprints, reference patterns, reusable components, and technical standards for AI delivery.
• Lead technical discovery, architecture workshops, design reviews, and proof-of-concept initiatives with engineering and customer teams.
• Define AI evaluation and observability approaches — grounding, accuracy, hallucination risk, latency, cost, and safety.
• Establish guardrails, human-in-the-loop controls, identity and access models, and Responsible AI practices.
• Mentor senior engineers and technical leads, and help build strong architecture practice across AI initiatives.

• 15+ years in software engineering, solution architecture, enterprise architecture, or cloud architecture, with recent hands-on technical work.
• A production GenAI or Agentic AI system youve personally architected and shipped — not a POC — using LLMs, RAG, tool/function calling, or multi-agent orchestration.
• Deep, hands-on cloud architecture experience on Azure, AWS, or GCP; multi-cloud experience is a strong plus.
• Working fluency with modern AI frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, or CrewAI.
• Strong software engineering foundation with hands-on programming, preferably Python, and the ability to review implementation-level decisions.
• Solid grounding in microservices, REST APIs, event-driven architecture, and enterprise integration patterns.
• Experience with relational, NoSQL, and vector data platforms, and designing data/knowledge access patterns for AI.
• Excellent communication — equally comfortable with engineering teams and business/technology leaders.

• Infrastructure-as-Code (Terraform, Bicep, CloudFormation), CI/CD, and production support practices.
• Hands-on exposure to Azure OpenAI/AI Foundry, AWS Bedrock, or Google Vertex AI/Gemini.
• Cloud architecture certification from Azure, AWS, or GCP.
• Experience building reusable AI platforms, accelerators, or enterprise AI capabilities.
• Exposure to Model Context Protocol (MCP), knowledge graphs, or advanced enterprise search.
• Background in customer-facing architecture, solutioning, or technical consulting.

• Own architecture decisions on high-visibility enterprise AI programs, from first workshop to production rollout.
• Work at the intersection of deep cloud engineering and cutting-edge agentic AI — not one at the expense of the other.
• Shape reusable patterns and standards that other architects and engineers will build on.
• Direct exposure to customer stakeholders and technology leadership, with real influence over technical direction.