Principal Solution Architect

Location: On-site – Hyderabad, India Employment Type: Full-Time Experience Level: 10+ years Industry: Technology / SaaS / Data Platforms About the Role We are looking for a Principal Solution Architect to define and drive the architecture of a scalable, high-performance, AI-native product platform . This role goes beyond traditional system design. You will operate at the intersection of technology, product, business, and AI , ensuring that systems are not only scalable and resilient but also intelligent, adaptive, and future-ready . You will play a critical role in shaping: What we build Why we build it How it evolves into an AI-augmented / AI-agent-driven platform Working closely with Product Managers, you will bring strong technical depth along with a forward-looking architectural vision , enabling the transition from deterministic systems intelligent, agent-driven systems . Key Responsibilities Architecture Leadership Own the end-to-end architecture of the product platform across backend, data, and frontend layers Define systems that are scalable, resilient, extensible, and cost-efficient Establish architectural principles, standards, and best practices across teams Introduce patterns for AI-native system design , including agent orchestration and inference pipelines Customer-Centric Thinking (Critical) Partner with Product Managers to deeply understand customer workflows, pain points, and usage patterns Translate customer problems into architecture for: Faster decision-making Reduced operational friction Improved user outcomes Communicate technical decisions in clear business terms Business-Aligned Architecture (Critical) Ensure all architectural decisions align with: Product strategy and roadmap Business goals (growth, scalability, cost, differentiation) Evaluate and guide trade-offs between: Speed vs scalability Cost vs performance Flexibility vs complexity Design systems that support: Long-term product evolution Monetization strategies AI-driven differentiation AI Agent–Driven Architecture (Core Focus) Define architecture for AI agents embedded within the platform , such as: Root Cause Analysis agents Anomaly detection agents Incident response / remediation agents Conversational assistants (natural language interfaces) Design agent orchestration frameworks , including: Multi-agent collaboration patterns Event-driven triggers and workflows Context propagation across systems Architect pipelines for: Data feature extraction model inference action Define integration patterns for: LLMs and ML models Vector stores and embeddings Real-time inference systems Ensure: Explainability and observability of AI decisions Guardrails, fallback mechanisms, and human override flows Balance deterministic systems with probabilistic AI behaviors Strong Point of View & Decision Making Bring clear, well-reasoned architectural opinions to discussions Challenge ideas with both technical depth and business context Confidently accept or reject approaches with structured justification: Technical feasibility and scalability Long-term maintainability Business impact and ROI AI model reliability and risk considerations Drive alignment across stakeholders with clarity and conviction Cross-Functional Leadership & Continuous Alignment Collaborate closely with Product Managers to stay continuously aligned with the product roadmap, priorities, and evolving business goals Provide early architectural input during feature ideation and roadmap planning Ensure architecture evolves in sync with roadmap changes, avoiding misalignment and rework Guide Engineering teams with clear architectural direction , balancing short-term delivery with long-term vision Act as a core partner in the Product–Architecture–Engineering triad Proactively identify and mitigate: Technical risks AI/ML risks (bias, drift, reliability) System Design & Technical Depth Design and evolve systems handling: High-volume data processing Real-time and batch workflows Scalable APIs and frontend systems Architect data + AI pipelines , including: Streaming ingestion Feature engineering Model inference layers Work across technologies such as: Distributed systems and microservices architectures NoSQL/SQL databases (MongoDB, Elasticsearch preferred) Modern web stacks (MERN or similar) Ensure efficient data flow from ingestion processing storage consumption Forward-Looking Architecture Partner with Product Managers to define long-term platform evolution Drive transition toward: AI-assisted systems Predictive insights Autonomous workflows Identify opportunities for: Platform extensibility Intelligent automation Reusable AI-driven components Build systems that are: Future-ready Adaptable to rapid AI advancements Governance & Execution Excellence Create HLDs and review & approve LLDs Drive design reviews, benchmarking, and capacity planning Establish governance for: Performance Reliability Security AI model lifecycle (versioning, evaluation, monitoring) Ensure adherence to standards across teams Required Skills: Technical Expertise 10+ years of experience in software engineering / architecture roles Strong experience designing scalable, distributed systems Hands-on expertise in: Backend systems and APIs Data platforms (SQL/NoSQL; MongoDB/Elasticsearch preferred) Modern frontend architectures (React or similar) AI & Data Systems (Important) Understanding of: AI/ML system design (LLMs, anomaly detection, recommendation systems) Real-time inference and batch ML pipelines Vector databases, embeddings, and semantic search Experience (or strong interest) in building AI-powered or agent-based systems Communication & Influence Exceptional ability to: Explain complex systems in simple, business-friendly language Influence senior stakeholders and engineering teams Strong written and verbal communication skills What We're Looking For A well-rounded architect who combines: Technical depth Product thinking Engineering alignment AI-first mindset Someone who can: Think like a customer Operate like a product partner Decide with the clarity of a senior architect Operate effectively within a Product–Architecture–Engineering triad model Good to Have Background in observability, monitoring, or data platforms Exposure to cloud-native architectures (Azure/AWS) Why This Role is Exciting Opportunity to architect a next-generation AI-native platform Lead the shift from: Systems of record Systems of insight Systems of action Work on cutting-edge problems across: Distributed systems Data platforms AI and autonomous systems