AI Architect

Job description

Job summary We are seeking an experienced AI Architect to design & deliver scalable, secure, & production-ready agentic AI solutions across Cyient. The role will translate business requirements into enterprise ai architectures, define technology standards & reusable patterns, evaluate emerging AI technologies, & provide technical leadership from ideation through production and scale. Key responsibilities • Design end-to-end architectures for AI/ML, generative AI, LLM, & agentic AI solutions across enterprise environments. • Translate business use cases & requirements into scalable AI solution architectures, including data flows, models, APIs, integrations, infrastructure, security, & governance. • Architect enterprise AI solutions using LLMs, rag, embeddings, vector databases, prompt engineering, tool/function calling, model routing, & LLM orchestration. • Design AI agent & multi-agent architectures, including agent planning, memory, tools, orchestration, human-in-the-loop, & enterprise system integration. • Define AI/ML architecture covering model development, training, inference, deployment, monitoring, & lifecycle management. • Evaluate & recommend foundation models, AI platforms, frameworks, vector databases, model-serving technologies, & cloud services based on performance, cost, scalability, security, & business requirements. • Establish AI reference architectures, reusable frameworks, design patterns, engineering standards, & accelerators. • Ensure AI solutions address responsible AI, security, privacy, compliance, access control, prompt injection, data leakage, hallucination, & model misuse. • Define AI evaluation & observability approaches covering accuracy, relevance, groundedness, latency, cost, safety, reliability, & model/agent performance. • Conduct technology assessments, prototypes, & POCs to evaluate emerging technologies including generative AI, agentic AI, multimodal AI, reasoning models, & AI infrastructure. • Provide architectural guidance & mentorship to AI engineers, ML engineers, data scientists, & development teams, & participate in architecture governance & reviews. • Collaborate with business leaders, enterprise architecture, solution architecture, data, cybersecurity, cloud, & engineering teams to ensure successful AI adoption & production deployment. Key qualifications • Bachelor's or master's degree in computer science, AI, data science, engineering, or related discipline. • 10+ years of experience in software engineering, solution architecture, AI/ML, data science, or related technology domains, with 5+ years designing & implementing enterprise AI solutions. • Strong hands-on experience with generative AI, LLMs, foundation models, rag, vector databases, embeddings, prompt engineering, LLM orchestration, & AI evaluation. • Demonstrated experience designing production-grade AI & agentic AI solutions. • Strong python programming skills & familiarity with modern AI/ML frameworks. • Strong foundation in software architecture, distributed systems, APIs, microservices, databases, enterprise integration, & cloud architecture. • Experience with one or more major cloud platforms such as Azure, AWS or google cloud. • Experience with AI/ML deployment, model serving, monitoring, MLOps/LLMOps, & lifecycle management. • Strong understanding of AI security, responsible AI, data privacy, and AI governance. • Ability to evaluate emerging AI technologies and translate them into practical, scalable enterprise architectures. • Strong communication, stakeholder-management, technical leadership, and mentoring skills.