Principal Solution Architect - Data, Azure & AI

Role: Principal Solution Architect - Data & AI

📍 Location: Hybrid/On-site (Hyderabad)

💼 Employment Type: Full-Time

🕒 Experience: 7+ Years

🏭 Industry: Technology / SaaS / Data Platforms / Azure / AI

About the Role

We are looking for a highly experienced 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 to 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

• 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

• Ensure all architectural decisions align with:

– Product strategy and roadmap

– Business goals (growth, scalability, cost optimization, 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 and remediation agents

– Conversational assistants and 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 ingestion –> Feature extraction –> Model inference –> Intelligent actions

• Define integration patterns for:

– LLMs and ML models

– Vector databases and embeddings

– Real-time inference systems

• Ensure:

– Explainability and observability of AI decisions

– Guardrails and fallback mechanisms

– Human override workflows

• 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 around:

– 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 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 rework and misalignment .
• Guide Engineering teams with clear architectural direction while balancing short-term delivery and long-term vision.
• Act as a core partner in the Product–Architecture–Engineering triad .
• Proactively identify and mitigate:

– Technical risks

– AI/ML risks including bias, drift, and reliability concerns

System Design & Technical Depth

• Design and evolve systems handling:

– High-volume data p…