New opportunity

Field Engineer - India

Cursor · Bengaluru, India

About this role

Cursor is seeking a Field Engineer in India to serve as the technical face of the company, partnering with prospective and strategic enterprise customers to drive the adoption of their AI coding agent. This role involves guiding customers through evaluations and proofs of concept to ensure they realize the full value of AI in their engineering workflows. Responsibilities include: Partnering with Account Executives to lead technical discovery, demos, and proof-of-concept engagements; Building trusted relationships with engineering leaders, architects, and developers; Translating customer requirements into solution designs, including best practices for rollout, prompt strategy, and context configuration; Developing and delivering technical presentations, whiteboard sessions, and workshops; Acting as a technical advocate internally to influence product roadmaps; Collaborating with post-sales teams for successful onboarding. Qualifications: Strong communication skills and executive presence; Deep understanding of the software development lifecycle, modern engineering organizational structures, and developer productivity challenges; Hands-on technical ability with fluency in technical workflows, AI tooling, and debugging complex environments; Experience in Field Engineering, Solutions Architecture, Pre-Sales, or Consulting for enterprise software; Ability to thrive in a fast-paced, resourceful startup environment.

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

Skills for this role

Field EngineeringSolutions ArchitecturePre-SalesConsultingSoftware Development LifecycleDeveloper ProductivityAI ToolingTechnical CommunicationPrompt StrategyTechnical DiscoveryProof of Concept

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 Proof-of-Concept Execution
The core of the role is guiding enterprise customers through evaluations of the AI coding agent. You must be able to lead technical discovery, perform demos, and manage proof-of-concept engagements that demonstrate tangible value in engineering workflows.
Strategic Technical Advisory
You are expected to build trusted relationships with engineering leaders and architects. This requires translating complex customer requirements into actionable solution designs, including prompt strategy and context configuration, while acting as a bridge between the customer and internal product teams.
Cross-Functional Product Advocacy
As the technical face of the company, you must synthesize customer feedback and technical challenges to influence Cursor's product roadmap. You will work closely with Sales, Product, and Engineering to ensure customer needs are represented internally.

Likely challenges to think through

Navigating Enterprise Resistance to AI Adoption
Enterprise engineering teams often have rigid workflows and security concerns regarding AI tools. You will likely face skepticism about code privacy or productivity claims. Prepare by developing a framework for addressing technical objections and demonstrating ROI through concrete, data-backed examples.
Balancing Technical Depth with Executive Communication
The role requires switching between deep-dive debugging with developers and high-level strategic presentations for CTOs. The difficulty lies in maintaining credibility with both audiences; prepare by practicing how to explain the business impact of AI tooling without losing the technical nuance required by engineering stakeholders.

Practice your answers

Technical practice

AI Coding Agent Integration Strategy

What to coverExplain how you would approach integrating an AI coding agent into a large enterprise codebase. Focus on balancing developer productivity with security, context configuration, and existing CI/CD workflows.

Technical Discovery and Scoping

What to coverDescribe your process for conducting a technical discovery session with a prospective enterprise customer. How do you identify their specific pain points in the software development lifecycle to tailor a proof-of-concept?

Prompt Engineering and Context Management

What to coverDiscuss best practices for prompt strategy and context configuration when deploying AI tools for enterprise teams. How do you guide customers to optimize their inputs for better code generation results?

Debugging AI-Assisted Workflows

What to coverHow do you approach troubleshooting issues when an AI tool produces unexpected or incorrect code in a complex, legacy environment? Explain your methodology for isolating whether the issue lies in the prompt, the context, or the underlying model.

Translating Technical Value to Business Impact

What to coverHow do you articulate the ROI of an AI coding tool to non-technical stakeholders or executives? Suggest focusing on metrics like developer velocity, onboarding time, and code quality improvements.

Competitive Positioning

What to coverHow do you maintain deep knowledge of the AI tooling landscape to effectively position a specific product against competitors? Suggest preparing a framework for comparing feature sets, integration capabilities, and developer experience.

Managing Proof-of-Concept Success

What to coverWhat criteria do you use to define a successful proof-of-concept for an enterprise software tool? Explain how you manage customer expectations and ensure the transition to a full rollout is seamless.

Internal Product Advocacy

What to coverDescribe how you would act as a bridge between customer feedback and the internal product team. How do you prioritize and communicate feature requests or bugs to ensure they align with the product roadmap?

Behavioural practice

Influencing Technical Stakeholders

What to coverShare an example of a time you convinced a skeptical engineering leader or architect to adopt a new tool. Focus on how you addressed their specific technical concerns and built trust.

Navigating Startup Ambiguity

What to coverDescribe a situation where you had to be resourceful in a fast-paced environment without clear processes. How did you identify the problem, take initiative, and deliver a solution?

Managing Conflicting Priorities

What to coverTell us about a time you had to balance the needs of multiple enterprise customers while also supporting internal product development. How did you manage your time and communicate trade-offs?

Handling Technical Objections

What to coverProvide an example of a high-stakes meeting where you faced significant technical pushback. How did you maintain your executive presence and steer the conversation toward a productive outcome?

Sharpen your knowledge

Topics to prepare

Software Development Lifecycle (SDLC) and Developer Productivity
Review how AI agents integrate into modern CI/CD pipelines and developer workflows. Exercise: Map out a typical enterprise engineering workflow and identify specific stages where an AI coding agent could reduce latency or improve code quality, then prepare to discuss these as business impact metrics.
Enterprise Solution Design and Rollout Strategy
Understand the challenges of deploying new developer tools in large organizations, including security, compliance, and team adoption. Exercise: Draft a hypothetical rollout plan for an enterprise team, focusing on how to configure context and prompt strategies to meet specific organizational coding standards.

Skills to revise

Technical Discovery and Pre-Sales Presentation
Practice translating complex technical features into value-driven narratives for diverse stakeholders, from developers to CTOs. Exercise: Prepare a 5-minute whiteboard session outline that explains the technical architecture of an AI coding agent to a non-technical executive, focusing on ROI and security.
Prompt Strategy and Context Configuration
Develop expertise in optimizing AI performance through effective context management. Exercise: Create a set of best-practice guidelines for configuring project-specific context (e.g., codebase indexing, documentation inclusion) to improve the accuracy of AI-generated code suggestions.
Technical Troubleshooting in Complex Environments
Demonstrate the ability to debug issues within integrated development environments and AI-assisted workflows. Exercise: Simulate a scenario where an AI agent provides suboptimal code in a complex, legacy codebase and document the steps you would take to diagnose the root cause, such as context limitations or prompt ambiguity.

Your preparation checklist

Prepare a technical narrative on tool adoption
Draft a structured response to the application question regarding convincing a technical audience to adopt a new tool. Focus on identifying specific resistance points, such as security concerns or workflow disruption, and detail the evidence-based approach you used to overcome them.
Develop a proof-of-concept framework for AI coding agents
Create a mock plan for a proof-of-concept engagement. Define the success metrics for an enterprise team adopting an AI coding agent, including how you would measure developer productivity gains and how you would configure context for a complex codebase.
Refine your executive presence and presentation style
Prepare a short, high-impact presentation that explains the value of AI-assisted development to both a CTO and a lead developer. Ensure you can articulate the technical benefits of prompt strategy and context configuration while maintaining a focus on business outcomes.
Analyze enterprise software development challenges
Document your understanding of modern engineering organizational structures and common bottlenecks in the software development lifecycle. Be prepared to discuss how AI tooling specifically addresses these challenges in large-scale, complex environments.
Formulate strategic questions for the interviewers
Prepare questions that demonstrate your interest in the role's impact, such as asking how the Field Engineering team currently influences the product roadmap or how they balance technical discovery with the need for rapid, scalable customer onboarding.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in India. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…