Solution Architect - AI for Software Engineering / Coding Automation

Job Description: Solution Architect – AI for Software Engineering / Coding Automation

Location: India

Role Type: Full-Time

Experience Level: Senior / Architect Level

Presales Experience: Mandatory

Work Model: Hybrid / Remote / Customer-aligned as required

1. Role Overview

We are looking for a highly experienced Solution Architect – AI for Software Engineering / Coding Automation to support enterprise customers in adopting AI-enabled software development practices. The ideal candidate should have strong software engineering architecture experience, hands-on understanding of AI coding tools, and proven presales capability.

This role requires a candidate who can work closely with sales teams, customer stakeholders, delivery teams, and engineering leadership to design and present solutions that improve developer productivity, code quality, application modernization, testing automation, Dev

Ops efficiency, and overall software delivery performance.

The candidate must be comfortable in a customer-facing role and should be able to lead discovery sessions, solution workshops, technical presentations, demos, proof-of-concepts, proposal responses, and architecture discussions.

2. Key Responsibilities

A. Solution Architecture and AI-Enabled Engineering

• Design AI-enabled software engineering solutions using modern AI coding and GenAI platforms.

• Advise customers on using AI across the software development lifecycle, including requirements analysis, code generation, code review, test generation, documentation, legacy modernization, Dev

Ops automation, and engineering knowledge management.

• Evaluate customer software engineering maturity and recommend AI adoption roadmaps.

• Define secure, scalable, and enterprise-ready architecture patterns for adopting AI coding assistants.

• Support application modernization initiatives using AI-assisted refactoring, migration, documentation, and automated testing.

• Identify opportunities to improve developer productivity, code quality, release velocity, and engineering efficiency.

• Create reusable solution assets, accelerators, reference architectures, and implementation playbooks.

B. Presales and Customer Engagement

• Lead customer-facing discovery sessions, technical workshops, solution presentations, and demos.

• Work closely with sales teams to qualify opportunities and shape AI/software engineering transformation proposals.

• Translate customer business and technology challenges into solution approaches and value propositions.

• Create technical proposals, solution decks, architecture diagrams, proof-of-concept plans, effort estimates, and implementation roadmaps.

• Support RFP/RFI responses and customer proposal submissions.

• Present AI productivity benefits, governance models, delivery approaches, and ROI to senior customer stakeholders.

• Partner with delivery teams to ensure proposed solutions are practical, scalable, and executable.

C. Delivery Advisory and Governance

• Define best practices for responsible AI adoption in software engineering teams.

• Establish guidelines for code security, intellectual property protection, data privacy, prompt governance, compliance, and enterprise AI usage.

• Support pilot programs and help scale successful AI adoption across customer engineering teams.

• Work with delivery teams to implement AI-assisted software development practices.

• Provide guidance on developer adoption, change management, training, and productivity measurement.

• Help define metrics to measure AI impact, including developer productivity, defect reduction, test coverage, release cycle improvement, and engineering quality.

3. Required Skills and Experience

• 10+ years of experience in software engineering, solution architecture, enterprise application development, or technology consulting.

• Mandatory presales experience in IT services, software engineering, cloud, digital transformation, application modernization, or AI solutions.

• Strong understanding of the software development lifecycle, Agile delivery, Dev

Ops, CI/CD, and modern engineering practices.

• Hands-on knowledge of AI coding and GenAI tools such as:

• Git

Hub Copilot

• Cursor

• Amazon Q Developer

• Azure OpenAI

• ChatGPT Enterprise

• Claude

• Google Gemini Code Assist

• Similar enterprise AI development tools

• Strong background in one or more technology stacks such as:

• Java / Spring Boot

• .NET / C#

• Python

• Java

Script / Type

Script / React / Angular

• Cloud-native applications

• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform.

• Ability to create architecture diagrams, technical proposals, solution documents, effort estimates, and implementation roadmaps.

• Strong understanding of software quality engineering, test automation, code review, security scanning, and engineering productivity metrics.

• Excellent communication, presentation, customer stakeholder management, and consulting skills.