AI Architect

AI Architect
Location: Bengaluru, India
Experience: 10 to 15 years (8+ years in AI/ML, 2–4 years in GenAI/LLM systems)
Role: AI Architect (Architecture Ownership)
About Aurigo
Aurigo is an AI-native capital program management platform trusted by over 300 customers managing more than $450 billion in capital programs across North America. With over 40,000 projects delivered, Aurigo helps organisations in transportation, water and utilities, healthcare, higher education, and government plan, build, and manage infrastructure with confidence. Recognised as one of the Top 25 AI Companies of 2024 and a Great Place to Work for three consecutive years, we leverage artificial intelligence to create smarter, more connected outcomes. At Aurigo, we don't just build software — we help shape the future of infrastructure.
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
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
Good to Have
Experience building enterprise AI platforms (not just single applications)
Exposure to AI governance, compliance, and ethical AI frameworks
Background spanning MLOps, platform engineering, or applied AI research
Familiarity with fine-tuning, RLHF, or model customization techniques
Experience collaborating with business functions such as Sales, HR, Finance, or Operations
Success Metrics
Production-grade AI systems achieving high accuracy, low latency, and controlled cost
Well-defined evaluation and monitoring frameworks adopted across teams
Reusable AI platform components deployed and leveraged by multiple engineering teams
Scalable AI architecture handling enterprise-scale data volumes and workflows
Measurable improvement in time-to-production for new AI initiatives
Why This Role Stands Out
True architecture ownership — take AI systems to Production not only POC work
Opportunity to define AI engineering standards and set the technical direction for the organization
High-visibility role driving enterprise AI transformation journey in an organization that is recognized as one of the Top 25 AI Companies — AI is core to the product, not a side project

Work arrangement
No