Principal Solution Architect

Location: On-site – Hyderabad, India

Employment Type: Full-Time

Experience Level: 10+ years

Industry: Technology / SaaS / Data Platforms

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.

• 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 

• 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

• 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

• 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

• 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

• 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)

• 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

• 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 

• 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

• 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)

• 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

• Exceptional ability to:
• Explain complex systems in simple, business-friendly language
• Influence senior stakeholders and engineering teams
• Strong written and verbal communication skills 

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

• Background in observability, monitoring, or data platforms
• Exposure to cloud-native architectures (Azure/AWS)

• 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