New opportunity

Solution Architect (AI / Data / Full Stack) - Senior Principal Consultant

Winfo Solutions · Hyderabad, India

About this role

We are looking for an experienced Solution Architect with strong expertise in AI, Data, Cloud, and Full Stack technologies. The candidate will be responsible for designing scalable, secure, and high-performing solutions and working closely with clients, business teams, and engineering teams.

Responsibilities

Design end-to-end technology solutions across AI, Data, Cloud, and Full Stack platforms.
Understand business and technical requirements and translate them into scalable solution architectures.
Define application architecture, technology stack, integration patterns, APIs, and data architecture.
Design solutions leveraging Generative AI, LLMs, Machine Learning, Data Engineering, and Analytics.
Develop cloud-native solutions using Azure, AWS, or GCP.
Provide technical leadership and guidance to development and engineering teams.
Review technical designs, code approaches, and architecture decisions.
Support POCs, technical evaluations, and solution demonstrations.
Collaborate with Presales and Sales teams on RFPs, proposals, estimations, and client presentations.
Ensure solutions meet requirements for security, scalability, performance, availability, and maintainability.
Stay updated with emerging technologies and recommend suitable solutions based on business needs.

Required qualifications

Strong experience in Solution Architecture and Enterprise Application Design.
Strong knowledge of AI/ML, Generative AI, LLMs, RAG, and AI application development.
Strong understanding of Data Engineering, Data Lakes, ETL/ELT, Databricks, Snowflake, and Analytics.
Good experience with Full Stack technologies such as Python/Java/.NET, React/Angular, APIs, and Microservices.
Strong knowledge of Azure, AWS, or GCP cloud platforms.
Experience with databases, distributed systems, integration, and API architecture.
Strong understanding of security, scalability, performance, and cloud architecture.
Excellent communication, problem-solving, and client-facing skills.
10+ years of overall experience in software engineering, architecture, or technology consulting.
Proven experience as a Solution Architect / Technical Architect across AI, Data, Cloud, or Full Stack technologies.

Preferred qualifications

Experience working with enterprise/global clients.
Experience in client-facing architecture discussions and technical workshops.
Experience in presales, RFP/RFI responses, and solution proposals.
Cloud, AI, Data, or Architecture certifications will be an added advantage.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Generative AILLMsMachine LearningData EngineeringPythonJava.NETReactAngularAzureAWSGCPDatabricksSnowflakeRAG

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Designing End-to-End Scalable Architectures
Translate business and technical requirements into robust, secure, and high-performing architectures spanning AI, Data, Cloud, and Full Stack platforms, ensuring alignment with availability and maintainability standards.
Integrating Advanced AI and Data Technologies
Design and validate scalable solutions leveraging Generative AI, LLMs, RAG, Machine Learning, Data Engineering, and analytics platforms like Databricks and Snowflake.
Providing Technical Leadership and Guidance
Drive technical direction, review architecture decisions and code approaches, and offer mentorship to engineering teams across cloud-native development in Azure, AWS, or GCP.
Collaborating on Presales and Client Engagement
Support technical evaluations, proofs of concept (POCs), solution demonstrations, and contribute directly to RFPs, proposals, estimations, and client-facing architecture workshops.

Likely challenges to think through

Balancing Broad Technical Breadth with Deep AI/Data Specialization
The role requires simultaneous expertise across Generative AI, cloud data platforms, multi-cloud ecosystems (Azure, AWS, GCP), and full-stack engineering. Preparation involves structuring architectural frameworks that seamlessly integrate fast-moving LLM and RAG workflows with enterprise-grade backend microservices.
Bridging Technical Execution with Presales and Client Communication
Navigating high-stakes client-facing discussions and contributing to RFPs and technical workshops while managing internal engineering oversight. Preparation requires refining communication strategies to translate complex AI architecture into clear business value for global enterprise clients.

Practice your answers

Technical practice

Designing RAG Systems and Managing LLM Hallucinations

What to coverDiscuss how you design a Retrieval-Augmented Generation (RAG) architecture for an enterprise application, focusing on vector database selection, chunking strategies, and mitigation of LLM hallucinations. Address tradeoffs between latency, retrieval accuracy, and token window constraints. Emphasize evaluation metrics used to assess output faithfulness and relevance.

Data Lakehouse Architecture with Databricks and Snowflake

What to coverExplain how you would architect a modern data engineering pipeline leveraging Databricks and Snowflake for large-scale analytics and machine learning workloads. Address ETL/ELT patterns, data governance, storage tiering, and performance optimization in distributed environments. Highlight how data architecture integrates with downstream AI applications.

Multi-Cloud Native Application Design across Azure, AWS, and GCP

What to coverCompare cloud-native service models across Azure, AWS, and GCP for building highly available, secure, and scalable microservices. Discuss strategies for managing cloud portability, avoiding vendor lock-in, and implementing robust API gateways and integration patterns. Address security and availability SLAs across different cloud providers.

Full-Stack Microservices Architecture and API Design

What to coverDescribe how you define application architecture and integration patterns using modern full-stack technologies like Python, Java, or .NET paired with React or Angular frontends. Address considerations for asynchronous messaging, API versioning, microservice decoupling, and state management. Discuss security best practices including OAuth2 and token-based authentication.

Ensuring Scalability, Security, and Performance in Enterprise Solutions

What to coverOutline your approach to reviewing technical designs and code approaches to ensure enterprise-grade security, scalability, and performance. Discuss load balancing strategies, caching layers, distributed database consistency models, and monitoring tools. Explain how you balance rapid feature delivery with non-functional architectural requirements.

Conducting Proofs of Concept (POCs) and Technology Evaluations

What to coverExplain your methodology for planning and executing technical evaluations and POCs for emerging AI or cloud technologies. Discuss how you define success criteria, measure performance benchmarks, and present findings to technical and business stakeholders. Highlight how POC results influence final architecture decisions.

Machine Learning Model Lifecycle and MLOps Integration

What to coverDiscuss how you integrate machine learning models and Generative AI services into production pipelines, covering deployment strategies, monitoring for model drift, and inference optimization. Address the pipeline orchestration required to bridge data engineering teams with application developers. Emphasize reproducibility and automated CI/CD practices for AI.

Translating Business Requirements into Scalable Solution Architectures

What to coverDescribe a structured process for eliciting ambiguous business requirements from clients and translating them into comprehensive application and data architectures. Discuss how you document architectural decisions, handle trade-offs between cost and performance, and align technology stacks with enterprise goals.

Behavioural practice

Managing Conflicting Priorities between Engineering Teams and Clients

What to coverDescribe a situation where you had to balance urgent client demands or sales commitments with technical debt or architectural integrity. Detail the actions you took to negotiate realistic deliverables and maintain alignment between engineering and business teams, and explain the resulting impact on project success.

Driving Alignment during High-Stakes Architecture Workshops

What to coverShare an example of leading a client-facing architecture discussion or technical workshop where stakeholders had conflicting technical visions. Explain how you structured the conversation, evaluated alternatives objectively, and guided the group toward a consensus solution. Highlight the outcome of the workshop.

Collaborating on Presales RFPs and Solution Estimations

What to coverRecall an experience collaborating with sales or presales teams to respond to an enterprise RFP or proposal. Describe your role in estimating timelines, scoping technical solutions, and presenting the proposal to executive stakeholders. Discuss how you handled uncertainty or missing information in the requirements.

Providing Technical Leadership and Mentorship to Engineering Teams

What to coverProvide an example of how you guided a development team through a complex architectural shift or the adoption of a new technology stack (such as Generative AI or cloud-native migration). Detail how you reviewed technical designs, provided constructive feedback, and fostered skill growth across the team.

Sharpen your knowledge

Topics to prepare

Enterprise Solution Architecture & Integration Patterns
Review core architectural styles (microservices, event-driven, API-first design) and enterprise security patterns. Exercise: Draft an end-to-end reference architecture diagram integrating a cloud-native backend with external API gateways, ensuring high availability, scalability, and security compliance. Relevance: Directly aligns with the core requirement to define application architecture, technology stacks, integration patterns, and APIs.
Generative AI and RAG System Design
Study retrieval-augmented generation (RAG) frameworks, LLM orchestrators, prompt engineering paradigms, and evaluation metrics for AI apps. Exercise: Design a secure enterprise chatbot architecture that connects private document stores with LLMs using vector databases and strict data governance. Relevance: Addresses the explicit requirement for strong knowledge of AI/ML, Generative AI, LLMs, RAG, and AI application development.
Data Engineering, Lakes, and Analytics Platforms
Review modern data architecture concepts including ETL/ELT pipelines, data lakes, and lakehouse design using Databricks and Snowflake. Exercise: Outline a data pipeline flow that ingests disparate enterprise data sources into a centralized lakehouse for downstream machine learning and analytics. Relevance: Fulfills the requirement for a strong understanding of Data Engineering, Databricks, Snowflake, and analytics.
Cloud-Native Infrastructure & Multi-Cloud Strategy
Examine cloud architectural principles across Azure, AWS, and GCP, covering compute, serverless, storage, and networking services. Exercise: Compare the serverless and container orchestration offerings across Azure, AWS, and GCP to justify selection criteria for a high-throughput enterprise workload. Relevance: Direct preparation for developing cloud-native solutions using Azure, AWS, or GCP.

Skills to revise

Databricks and Snowflake Data Platform Implementation
Practice querying, managing data warehouse models, and setting up data pipelines in Databricks or Snowflake. Exercise: Write a sample Spark or SQL notebook configuration for loading and transforming large-scale datasets within a Snowflake or Databricks environment. Relevance: Directly supports the hands-on data engineering and analytics tooling specified in the required skills.
Full-Stack API and Microservices Development (Python / Java / .NET)
Build and secure RESTful APIs or microservices using Python (FastAPI/Flask), Java (Spring Boot), or .NET Core, paired with frontend frameworks like React or Angular. Exercise: Implement a secure microservice endpoint with token-based authentication and connect it to a front-end component. Relevance: Aligns with the requirement for hands-on experience across full-stack technologies, APIs, and microservices.
Presales Solution Proposing and RFP Estimation
Practice structuring technical proposals, scoping architectures, and presenting technical solutions to prospective clients. Exercise: Draft a mock response to an enterprise RFP outlining solution phases, technical risks, cloud infrastructure estimations, and delivery timelines. Relevance: Directly targets preferred qualifications and responsibilities involving presales, RFPs, client presentations, and technical workshops.

Your preparation checklist

Prepare an End-to-End AI and Data Architecture Walkthrough
Create a comprehensive system diagram and case study presentation demonstrating a solution that integrates Generative AI, LLMs, RAG, and data engineering pipelines (utilizing Databricks/Snowflake) on a cloud platform (Azure, AWS, or GCP). Completion means having a structured walkthrough ready to explain trade-offs in scalability, security, and latency.
Document Multi-Cloud and Full-Stack Design Decision Records
Compile examples of past technical decisions documenting the choice of cloud services, microservices design patterns, API architectures, and full-stack tech stacks (Python, Java, or .NET with React/Angular). Completion means having concise documentation illustrating how you balance non-functional requirements like performance and maintainability.
Draft a Mock Enterprise RFP and Solution Proposal
Prepare a sample response to a complex client RFP, outlining proposed solution phases, technical estimations, risk mitigations, and client-facing presentation frameworks. Completion means having a ready artifact that reflects experience collaborating with presales and sales teams on proposals.
Structure STAR Examples for Client-Facing Leadership and Workshops
Write down concrete situational stories detailing how you led technical workshops, resolved conflicting stakeholder opinions, and mentored engineering teams through complex architectural migrations. Completion means having clear narratives ready that emphasize communication, problem-solving, and client management.
Formulate Thoughtful Employer Questions
Prepare a list of targeted questions about the company's enterprise client base, typical AI and data project lifecycles, and how the architecture team collaborates with presales and engineering delivery units. Completion means having insightful questions that demonstrate a deep understanding of the Solution Architect role.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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