New opportunity

Presales (Data, AI & Fullstack) - Senior Consultant

Winfo Solutions · Hyderabad, India

About this role

Winfo Solutions is seeking a Senior Consultant for a Presales role focused on Data, AI, and Fullstack technologies. The successful candidate will support business development by designing technical solutions, preparing proposals, and engaging with clients throughout the sales cycle.

Responsibilities

Understand client business and technical requirements and translate them into effective technology solutions.
Develop solution approaches, architectures, and technical proposals across Data, AI, and Full Stack technologies.
Prepare RFP/RFI responses, proposals, presentations, SOWs, and solution documents.
Conduct client presentations, technical discussions, demos, and solution walkthroughs.
Collaborate with Sales, Delivery, Architects, and Engineering teams to create winning solutions.
Identify opportunities to leverage Generative AI, Machine Learning, Data Engineering, Cloud, and Full Stack technologies.
Provide effort estimates, high-level project plans, and technology recommendations.
Support proof-of-concepts (POCs) and technical demonstrations.
Stay updated with emerging technologies and industry trends in AI, Data, Cloud, and Full Stack development.
Participate in client workshops and requirement-gathering sessions.
Support the sales team in identifying new opportunities and strengthening existing client relationships.

Required qualifications

8 years of overall experience in technology, solutioning, consulting, or presales.
Strong understanding of Data Engineering, AI/ML, Generative AI, and Full Stack development.
Knowledge of Python, Java/.NET, React/Angular, APIs, Microservices, and databases.
Good understanding of cloud platforms such as Azure, AWS, or GCP.
Knowledge of data technologies such as Databricks, Snowflake, Data Lake, ETL/ELT, and BI tools.
Understanding of AI/ML frameworks, LLMs, GenAI, RAG, and AI application development.
Strong solution architecture and technical documentation skills.
Excellent communication, presentation, and client-facing skills.
Ability to work effectively with Sales, Delivery, and Technical teams.

Preferred qualifications

Relevant experience in Data / AI / Full Stack Presales or Solution Architecture.
Experience handling RFP/RFI/RFQ and large enterprise proposals.
Experience in client-facing roles and technical presentations.
Experience supporting sales pursuits in the US/global market.
Relevant cloud or AI certifications.

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

Skills for this role

Data EngineeringAI/MLGenerative AIFull Stack DevelopmentPythonJava.NETReactAngularAPIsMicroservicesDatabasesAzureAWSGCPDatabricksSnowflakeData LakeETL/ELTBI toolsLLMsRAGSolution ArchitectureTechnical DocumentationPresales

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

Technical Solutioning and Architecture
The core of this role is translating complex client business requirements into actionable technical architectures. You must be able to bridge the gap between high-level business needs and specific technology stacks, including Data Engineering, AI/ML, and Full Stack development, while ensuring the proposed solutions are feasible and scalable.
Proposal Development and RFP Management
A significant portion of the role involves creating compelling RFP/RFI responses, SOWs, and technical proposals. You must demonstrate the ability to articulate value propositions clearly, provide accurate effort estimates, and develop high-level project plans that align with both client expectations and internal delivery capabilities.
Cross-Functional Collaboration
Success depends on your ability to act as a conduit between Sales, Delivery, Architects, and Engineering teams. You must facilitate communication to ensure that the solutions proposed during the presales phase are technically sound and can be successfully executed by the delivery teams.
Client Engagement and Technical Advocacy
You will lead client-facing activities, including workshops, technical presentations, and solution walkthroughs. This requires not only deep technical knowledge of GenAI, Cloud, and Data platforms but also the soft skills to influence stakeholders and build long-term relationships.

Likely challenges to think through

Balancing Technical Depth with Sales Velocity
The challenge lies in providing high-quality, detailed technical architectures under the tight deadlines typical of RFP cycles. To prepare, practice condensing complex technical concepts into persuasive, high-level summaries that address business outcomes rather than just technical features.
Managing Scope and Estimation Accuracy
Providing accurate effort estimates and project plans without a fully defined scope is a common presales difficulty. Prepare by developing a structured framework for estimation that accounts for common risks in Data and AI projects, such as data quality issues or model integration complexities.
Aligning Diverse Stakeholder Expectations
You will often need to reconcile the aggressive promises of a sales team with the realistic delivery constraints of engineering teams. Prepare by demonstrating your experience in negotiation and your ability to manage expectations by clearly defining the boundaries of a proposed solution.

Practice your answers

Technical practice

Architecting Generative AI Solutions

What to coverExplain how you would design a RAG-based architecture for an enterprise client. Discuss the trade-offs between vector database selection, embedding models, and the integration of LLMs into existing full-stack applications.

Data Engineering Pipeline Design

What to coverDescribe your approach to designing an end-to-end data pipeline using tools like Databricks or Snowflake. Focus on how you ensure data quality, scalability, and efficiency when moving data from raw sources to a consumption-ready Data Lake.

Full Stack and Microservices Integration

What to coverHow do you approach the architecture of a modern full-stack application using microservices? Discuss the role of APIs in connecting front-end frameworks like React or Angular to backend services built in Java or .NET.

Cloud Platform Selection Strategy

What to coverWhen proposing a cloud solution on Azure, AWS, or GCP, what criteria do you use to recommend one over the others? Address factors such as cost, existing client infrastructure, and specific service availability for AI/ML workloads.

Effort Estimation for Complex Projects

What to coverDescribe your methodology for providing high-level project plans and effort estimates during the presales phase. Suggestion: Focus on how you account for technical debt, integration risks, and the iterative nature of AI development.

RFP/RFI Response Strategy

What to coverWhat is your process for structuring a winning technical proposal for a large enterprise RFP? Explain how you balance technical depth with business value to ensure the solution resonates with non-technical stakeholders.

Proof-of-Concept (POC) Execution

What to coverHow do you define the scope of a POC to demonstrate value without over-committing resources? Discuss how you measure success and transition a successful POC into a full-scale production project.

AI/ML Framework Selection

What to coverCompare the use cases for traditional Machine Learning versus Generative AI/LLMs in a business context. Explain how you decide which technology is appropriate for a client's specific problem statement.

Behavioural practice

Managing Conflicting Stakeholder Requirements

What to coverDescribe a situation where you had to reconcile different technical requirements from a client's business team and their internal IT department. How did you facilitate a consensus while maintaining the integrity of the proposed solution?

Handling Technical Objections in Presentations

What to coverProvide an example of a time you faced a difficult technical question or objection during a client demo. How did you address the concern while maintaining client confidence and moving the sales process forward?

Collaborating Across Cross-Functional Teams

What to coverGive an example of how you have successfully collaborated with Sales, Delivery, and Engineering teams to win a deal. Highlight how you ensured that the solution you proposed was both technically viable and deliverable.

Adapting to Rapidly Evolving Technologies

What to coverDescribe how you stay updated with emerging trends in AI and Cloud. Provide an example of how you applied new knowledge to improve a solution or proposal for a client.

Sharpen your knowledge

Topics to prepare

Solution Architecture for AI and Data
Review principles of designing scalable architectures that integrate Data Engineering pipelines with AI/ML models. Exercise: Create a high-level architecture diagram for a RAG-based application that utilizes a cloud data lake and vector database. This is relevant for translating client requirements into technical proposals.
Presales Lifecycle and Proposal Management
Understand the structure of RFP/RFI responses and SOWs for large enterprise deals. Exercise: Draft a mock executive summary for a proposal that addresses a client's need for a modern data platform migration. This is essential for the core responsibility of preparing winning technical proposals.
Generative AI and RAG Frameworks
Deepen knowledge of LLM integration, prompt engineering, and Retrieval-Augmented Generation (RAG) patterns. Exercise: Compare the architectural trade-offs between fine-tuning a model versus implementing a RAG pipeline for enterprise data. This is critical for identifying opportunities to leverage GenAI in client solutions.

Skills to revise

Technical Presentation and Demo Delivery
Practice articulating complex technical concepts to non-technical stakeholders. Exercise: Prepare a 10-minute walkthrough of a hypothetical AI-driven solution, focusing on business value and ROI. This directly supports the requirement for conducting client presentations and demos.
Effort Estimation and Project Planning
Develop techniques for providing accurate high-level project plans and effort estimates. Exercise: Break down a sample project scope into phases, milestones, and resource requirements. This is a key requirement for supporting the sales team with realistic project timelines.
Cloud and Data Technology Proficiency
Maintain familiarity with cloud-native data services on platforms like Azure, AWS, or GCP. Exercise: Map specific services (e.g., Databricks, Snowflake, or cloud-native ETL tools) to common enterprise data challenges. This is necessary for providing informed technology recommendations during client engagements.

Your preparation checklist

Prepare a portfolio of technical solution architectures
Create 2-3 high-level architectural diagrams and accompanying summaries that demonstrate your ability to integrate Data Engineering, AI/ML, and Full Stack components. Focus on how you have previously designed scalable solutions using cloud platforms like AWS, Azure, or GCP.
Develop a case study on RAG and Generative AI implementation
Draft a concise walkthrough of a project where you applied LLMs or RAG frameworks to solve a business problem. Be prepared to explain the technical trade-offs, the choice of data stack (e.g., Snowflake, Databricks), and how you translated these technical choices into business value.
Refine your RFP and proposal response methodology
Prepare a summary of your process for responding to complex RFPs. Include how you approach effort estimation, project planning, and the creation of SOWs while collaborating with cross-functional teams like Sales and Delivery.
Practice a technical solution walkthrough
Prepare a 10-minute presentation simulating a client demo. Focus on explaining complex technical concepts—such as microservices or ETL pipelines—to non-technical stakeholders, ensuring you highlight the 'why' behind your technology recommendations.
Formulate strategic questions for the interviewers
Prepare questions regarding the current sales cycle at Winfo Solutions, such as: 'How does the presales team currently collaborate with the delivery team to ensure the feasibility of proposed AI solutions?' or 'What are the most common technical challenges clients are facing when adopting GenAI in their current infrastructure?'

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