AI - Architect

Long Description Role Overview: We are hiring an AI Architect to lead the design and evolution of enterprise-grade AI platforms and GenAI systems at scale. This is a high-impact, architecture-first role focused on solving real-world AI problems beyond POCs—owning system design, production maturity, evaluation frameworks, and governance. You will define how AI systems are built, deployed, observed, and scaled across the organization. Key Responsibilities: 1. Architecture & System Design Define end-to-end architecture for LLM-powered platforms, copilots, and agent-based systems Design scalable RAG architectures (retrieval, grounding, response orchestration) Architect multi-agent systems integrating enterprise tools, APIs, and workflows 2. GenAI Platform & Knowledge Systems Own the design of enterprise knowledge systems powered by LLMs and vector databases Implement advanced retrieval strategies (hybrid search, re-ranking, context optimization) Design memory, context management, and reasoning pipelines for complex workflows Optimize systems for accuracy, latency, reliability, and cost at scale 3. Evaluation, Observability & Governance Define and implement evaluation frameworks for RAG systems, agents, and copilots Establish AI observability including traceability, monitoring, and feedback loops Build guardrails, hallucination mitigation, and responsible AI controls 4. Cloud, MLOps & Production Engineering Architect deployment pipelines on AWS (Bedrock, OpenAI, and equivalent services) Design systems for scale, resilience, and high availability using microservices Ensure production readiness: monitoring, rollback strategies, and cost optimization 5. Technical Leadership & Strategy Act as the AI/GenAI technical authority across engineering teams Mentor engineers and guide teams on best practices, trade-offs, and design choices Drive AI roadmap, platform vision, and enterprise adoption strategy Requirements: Required Qualifications: 10–15 years of experience in software engineering, AI, or data platforms (8+ years in AI/ML, 2–4 years in GenAI/LLM systems) Proven track record designing and deploying production-grade AI/ML systems at scale Hands-on ownership of GenAI / LLM-based systems in production environments Core Technical Expertise RAG architectures (end-to-end: ingestion → retrieval → generation) LLM orchestration, prompting strategies, and system design Vector databases and retrieval optimization Agent frameworks such as LangChain, CrewAI, AutoGen, or equivalent Evaluation frameworks and metrics for AI systems AI observability, monitoring, and performance tuning Cloud platforms (AWS/Azure) and container orchestration (Kubernetes) Python / .Net and relevant ML/AI libraries and tooling About Aurigo Aurigo provides AI-native platforms that help capital owners connect planning, construction, and operations in a single environment, improving decision-making and execution. Trusted by…