New opportunity

Forward Deployed Engineer

Cisco · Bangalore, India

About this role

The Splunk Agent Resilience team at Cisco is defining the future of AI resilience by providing scalable, cost-effective evaluation and guardrails to ensure AI agents behave as intended. As a Forward Deployed Engineer (FDE), you will work at the intersection of software engineering, customer engagement, and product innovation to help large enterprises deploy AI into production. You will partner with customers to solve complex technical challenges while collaborating with internal teams to improve the platform based on real-world needs. Responsibilities include designing scalable AI solution architectures, guiding customers through production deployments, building integrations and prototypes, leading technical workshops, and creating reusable best practices. Minimum qualifications include a Bachelor's degree in Computer Science or related field, 4+ years of software engineering experience, proficiency in APIs, distributed systems, cloud platforms, or backend development, and hands-on experience with AI tools like Claude Code, Codex, or Cursor. Preferred qualifications include experience deploying Generative AI, LLM, or Machine Learning applications in production, familiarity with Kubernetes, observability, MLOps, and experience in consulting or solutions engineering. You must possess strong troubleshooting skills and excellent communication abilities to work directly with enterprise customers.

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

Skills for this role

Software EngineeringAPIsDistributed SystemsCloud PlatformsBackend DevelopmentClaude CodeCodexCursorGenerative AILLMMachine LearningKubernetesObservabilityMLOpsPythonJava

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

Production-Grade AI Deployment
The role centers on guiding large enterprises through the transition of AI agents from prototype to production. You must be able to design scalable architectures that incorporate guardrails and evaluation frameworks to ensure reliability, as the team's core mission is AI resilience.
Technical Customer Advocacy
You will act as the bridge between external enterprise customers and internal product teams. This requires translating complex business goals into technical requirements and effectively communicating real-world feedback to influence the product roadmap.
Scalable Tooling and Integration
Beyond individual deployments, you are expected to build reusable integrations, prototypes, and best practices. The goal is to create assets that accelerate success for future customers, requiring a focus on modular, maintainable code.

Likely challenges to think through

Balancing Customization with Scalability
Forward Deployed Engineers often face the tension between building bespoke solutions for specific enterprise clients and creating standardized, reusable platform features. Prepare by demonstrating how you have previously built tools that solved a specific client problem while remaining generalizable for broader use.
Navigating Ambiguity in AI Production
Deploying Generative AI and LLMs in production environments is inherently unpredictable. You will need to troubleshoot issues across the entire stack, from infrastructure to model behavior. Prepare by highlighting your experience with observability, MLOps, or debugging distributed systems under pressure.

Practice your answers

Technical practice

Designing Scalable AI Architectures

What to coverExplain your approach to designing AI solution architectures that handle enterprise-scale traffic. Focus on how you ensure reliability, latency management, and cost-effectiveness when deploying LLM-based applications in production environments.

AI Agent Guardrails and Reliability

What to coverDiscuss the technical strategies you would implement to ensure AI agents behave as intended. Address how you would design evaluation frameworks and guardrails to mitigate risks and improve the reliability of AI-powered applications.

Troubleshooting Distributed Systems

What to coverDescribe your methodology for troubleshooting complex issues in a distributed system. Highlight how you utilize observability tools and logs to isolate failures across different layers of the technology stack.

Production Deployment of Generative AI

What to coverWhat are the primary technical challenges when moving a Generative AI or LLM application from a prototype to a production environment? Discuss considerations such as model serving, API integration, and maintaining performance at scale.

Kubernetes and MLOps Integration

What to coverExplain how you would leverage Kubernetes and MLOps practices to manage the lifecycle of AI models. Focus on how these tools facilitate consistent deployments, monitoring, and automated scaling for AI services.

API Design for AI Integrations

What to coverWhen building integrations for enterprise customers, how do you ensure your APIs are secure, scalable, and easy to consume? Discuss your process for designing robust interfaces that connect AI agents with existing enterprise infrastructure.

Utilizing AI Development Tools

What to coverDescribe your experience with AI-assisted coding tools like Claude Code, Codex, or Cursor. Explain how you integrate these tools into your development workflow to accelerate prototyping and improve code quality.

Observability in AI Pipelines

What to coverHow do you implement observability for AI-driven applications to gain insights into model performance and agent behavior? Suggest metrics and monitoring strategies that help identify drift or unexpected outputs in production.

Behavioural practice

Managing Ambiguous Customer Requirements

What to coverDescribe a situation where you had to work with a customer who had unclear or evolving technical requirements. Explain how you clarified their goals, managed expectations, and delivered a successful technical solution.

Collaborating Across Teams

What to coverGive an example of how you have collaborated with internal Product and Engineering teams to translate customer feedback into product improvements. Focus on how you communicated the 'why' behind the customer's needs to influence the roadmap.

Leading Technical Workshops

What to coverDescribe a time you led a technical workshop or architecture discussion with enterprise stakeholders. How did you ensure the audience understood the technical concepts and felt confident in the proposed solution?

Handling High-Pressure Production Issues

What to coverTell me about a time you had to troubleshoot a critical issue in a production environment while under pressure. How did you coordinate with the team and the customer to resolve the problem while maintaining clear communication?

Sharpen your knowledge

Topics to prepare

AI Agent Resilience and Guardrails
The role focuses on evaluating and securing AI agents. Review concepts of LLM guardrails, output validation, and reliability frameworks. Exercise: Research common failure modes in production LLM applications (e.g., hallucinations, prompt injection) and design a conceptual architecture for a guardrail system that monitors and filters agent responses.
Distributed Systems Architecture
As a Forward Deployed Engineer, you must design scalable solutions for enterprise environments. Review principles of microservices, load balancing, and fault tolerance. Exercise: Sketch a high-level architecture for a distributed AI application that ensures high availability and low latency when processing large-scale enterprise data requests.
Consultative Engineering
The position requires bridging the gap between customer needs and product capabilities. Review techniques for technical discovery and translating business goals into technical requirements. Exercise: Practice explaining a complex technical concept (like MLOps pipelines) to a non-technical stakeholder, focusing on the business value and risk mitigation.

Skills to revise

AI Development Tools (Claude Code, Cursor, Codex)
The role requires hands-on experience with modern AI-assisted coding tools. Exercise: Use one of these tools to refactor a legacy Python or Java function into a more modular, production-ready service, documenting how the tool improved your development velocity.
Observability and MLOps
Monitoring AI in production is a key responsibility. Review standard observability patterns for LLMs, such as tracking latency, token usage, and drift. Exercise: Configure a simple monitoring dashboard for a local LLM application to track request success rates and response times.
Kubernetes and Cloud Infrastructure
Production deployments often leverage container orchestration. Exercise: Create a basic Kubernetes deployment manifest for a Python-based backend service, including health checks and resource limits, to demonstrate familiarity with containerized production environments.

Your preparation checklist

Map your experience to AI resilience and observability
Prepare to discuss how you have managed production-grade applications, specifically focusing on troubleshooting distributed systems and implementing observability. Be ready to explain how you would apply these skills to monitor and provide guardrails for AI agents.
Develop a technical project walkthrough for AI deployment
Prepare a concise case study of a production deployment you led. Highlight the architecture, the specific challenges you solved, and how you ensured the system was scalable and reliable. Emphasize your use of Python or Java and cloud infrastructure.
Demonstrate hands-on proficiency with AI development tools
Be prepared to discuss your practical experience with tools like Claude Code, Codex, or Cursor. Explain how you have used these to accelerate development, prototype integrations, or improve code quality in your previous roles.
Prepare for customer-facing technical scenarios
Since this is a forward-deployed role, practice explaining complex technical concepts to non-technical stakeholders or enterprise clients. Prepare examples of how you have gathered requirements, managed expectations, and translated business goals into technical architectures.
Formulate questions about the Splunk Agent Resilience platform
Prepare thoughtful questions for the interviewers regarding the current technical hurdles in AI agent guardrails, how the team balances custom customer integrations with platform scalability, and how the feedback loop between FDEs and internal product teams functions.

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