Senior Architect - .Net Agentic AI

Senior Software Architect - Senior AI Software Engineering Expert

We are a digitally native company that helps organizations reinvent themselves and unleash their potential. We are the place where innovation, design and engineering meet scale.Globant is 20 years old, NYSE listed public organization with more than 30,000 employees worldwide working out of 36 countries globally.

www.globant.com

Job Description: 

Location: Bangalore - Whitefield, Pune - Hijewadi phase - I OR Magapatta

Experience: 14-18 Years

Primary Technical Skills: .NET Core technologies, Azure,  Docker, Kubernetes,   

                                              AI, AgentiAI, AI coding tools (like GitHub Copilot, Claude, Codex)   

                                              Microservice Architecture, Project Governance 

                                              End to end solutioning, proposals, estimation. 

Good To Have : RFP, Presales, Proposals, estimation.

We are looking for a highly experienced software engineering expert who combines strong architecture and implementation skills with practical, disciplined use of AI-assisted development tools.This is not a generic "prompting" role. We need someone who can work across architecture, implementation, review, testing, and delivery, while also training other engineers to use AI in a controlled, repeatable, and production-ready way.

The successful candidate will help teams move beyond ad hoc AI usage. They will introduce and coach engineers on a structured AI-assisted engineering workflow that includes planning before implementation, clear constraints, reusable prompting patterns, visible review, validation, and traceability from requirements to delivered code.

You should be equally comfortable writing production code, reviewing system design, improving developer workflows, and coaching engineers on how to use tools such as Cursor, GitHub Copilot, Claude Code, Cline, or similar platforms responsibly and effectively.

What You Will Own

• Lead hands-on software engineering work across design, implementation, refactoring, testing, and delivery for cloud-based business applications.

• Shape solution architecture for scalable, secure, maintainable platforms, with a strong focus on .NET, Azure, APIs, and distributed systems.

• Define and roll out a practical AI-assisted engineering methodology that teams can reuse across projects.

• Train engineers to use AI with discipline, not just speed, including how to plan work, constrain execution, review outputs, validate results, and maintain traceability.

• Create reusable prompts, templates, working agreements, commands, or playbooks that reduce inconsistent one-off prompting.

• Coach teams on how to break work down into structured execution flows such as feature-by-feature, layer-by-layer, or parallelised implementation where appropriate.

• Review AI-generated plans, code, and artifacts for correctness, maintainability, architecture fit, naming, file placement, dependencies, error handling, and operational impact.

• Establish quality checks so AI-assisted output is validated through tests, reviews, and requirement-based verification rather than accepted at face value.

• Support modernisation initiatives, including migration from legacy solutions to modern cloud-native platforms.

• Contribute to DevOps, CI/CD, observability, operational readiness, and engineering governance.

• Help teams adopt a delivery model where AI usage is reviewable, explainable, and aligned with enterprise standards.
  
  

AI Engineering Expectations for This Role

You must be able to teach and model a controlled AI-SDLC approach, including:

• Planning before implementation rather than jumping straight into code generation.

• Iterative refinement of plans based on review and feedback.

• Clear constraints for AI execution, including requirements, architecture decisions, coding rules, file references, and expected outputs.

• Use of reusable prompts, commands, skills, or playbooks instead of relying on informal ad hoc instructions.

• Structured execution strategies that make AI work easy to follow and review.

• Visible review of AI-generated code and artifacts, with clear reasoning about what is accepted, rejected, or changed.

• Validation through tests, automated checks, and requirement-level inspection.

• Traceability between requirements, plan items, files changed, and implementation outcomes.
  
  

This role requires someone who can train others in these practices, inspect whether they are being followed, and raise the maturity of the engineering organisation over time.Key Responsibilities

• Deliver high-quality production code and architecture contributions in a hands-on capacity.

• Partner with product, architecture, and engineering teams to define practical AI-enabled ways of working.

• Run workshops, coaching sessions, and pairing sessions to train engineers on effective AI-assisted software delivery.

• Create examples, reference implementations, and reusable artifacts that teams can adopt directly.
  Evaluate AI development tools, workflows, and guardrails through technical proof-of-concepts.

• Define what good looks like for AI-generated plans, code reviews, validation evidence, and traceability outputs.

• Review and improve engineering practices covering design quality, coding standards, testing, deployment readiness, and maintainability.

• Support observability, monitoring, automation, and operational readiness as part of the full delivery lifecycle.

• Help teams make sound technical decisions and retain human control over important architectural and product-impacting choices.
  
  

Required Experience

• 14+ years of experience in software development, technical architecture, or full-stack engineering.

• Strong hands-on experience with .NET Core / .NET, C#, Azure, and cloud-native application design.

• Experience designing and delivering distributed systems, API-based solutions, and integration-heavy platforms.

• Demonstrated practical experience using AI-assisted development tools in real engineering work, not only experimentation.

• Proven ability to define or improve engineering workflows across planning, coding, testing, review, and release.

• Experience coaching, mentoring, or training engineers on technical practices, developer tooling, or delivery methods.

• Strong understanding of software design principles such as SOLID, DDD, OOP, and common design patterns.

• Experience with CI/CD, DevOps practices, code review, testing strategies, and production support.
  Ability to assess AI-generated output critically and explain trade-offs, risks, and corrective actions.
  Strong communication skills and ability to work effectively with cross-functional teams

Preferred Experience

• Experience establishing engineering standards, playbooks, or governance for AI-assisted development.

• Experience creating reusable prompt libraries, command systems, templates, or developer enablement assets.

• Experience in observability, monitoring, and alerting solutions.

• Exposure to frontend technologies such as Angular or React.

• Experience with Kubernetes / AKS.

• Experience supporting legacy-to-modern platform migration.

• Azure certifications such as AZ-305 or AZ-104.
  
  

What Strong Candidates Will Demonstrate

• They can show how they use AI to accelerate delivery without losing architectural control or engineering quality.

• They can explain how they plan work before implementation and how they refine that plan during execution.

• They can show examples of constraining AI with clear requirements, file references, coding rules, and expected outputs.
  They can demonstrate a repeatable workflow for AI-assisted delivery rather than relying on one-off prompting.
  They can explain how they review AI-generated code, validate it, and connect implementation results back to requirements.

• They are capable of teaching these practices clearly and raising the capability of other engineers.

Why This Role Matters

We are looking for someone who can improve software delivery in two ways at the same time: by contributing directly as a senior engineer and by helping the wider team adopt a disciplined, scalable, and auditable way of working with AI.

At Globant, we believe in fostering a diverse and inclusive workplace where everyone feels valued and respected. We are an Equal Opportunity Employer committed to creating a thriving and inclusive environment for all employees and candidates, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, veteran status, or any other legally protected characteristic. If you need any assistance or accommodations due to a disability, please let us know by applying through our Career Site or contacting your assigned recruiter. 

We may use AI and machine learning technologies in our recruitment process. Compensation is determined based on skills, qualifications, experience, and location. In addition to competitive salaries, we offer a comprehensive benefits package. Learn more about our commitment to diversity and inclusion and Globant’s Benefits.