New opportunity

Senior Forward Deployed Engineer

Cisco · Bangalore, India

About this role

Meet the Team: Join a team that is helping some of the world's largest enterprises successfully deploy AI into production. As a Forward Deployed Engineer (FDE), you will work at the intersection of software engineering, customer engagement, and product innovation. You will partner closely with customers to solve complex technical challenges while collaborating with Product and Engineering teams to improve the platform based on real-world customer needs. You will work on challenging AI deployments, partnering with Fortune 100 organizations to bring cutting-edge AI systems into production. Your Impact: Partner with enterprise customers to understand technical requirements and business goals. Design scalable AI solution architectures and guide customers through production deployments. Build integrations, prototypes, and reusable tools that accelerate customer success. Lead technical workshops, architecture discussions, and troubleshoot complex production issues. Collaborate with Product and Engineering teams to share customer feedback and influence future product enhancements. Create documentation, reusable best practices, and technical assets that benefit future deployments. Minimum Qualifications: Bachelor's degree in Computer Science or a related technical field. 7+ years of strong software engineering experience building production-grade applications (or Masters + 4 years, or PhD + 1 year). Experience with APIs, distributed systems, cloud platforms, or backend development. Strong troubleshooting and problem-solving skills across multiple layers of the technology stack. Excellent communication skills and the ability to work directly with enterprise customers. Comfortable working in fast-paced, ambiguous environments. Interest in AI, Machine Learning, or developer platforms. Preferred Qualifications: Experience deploying or supporting Generative AI, LLM, or Machine Learning applications in production. Experience with Kubernetes, cloud infrastructure, observability, or MLOps. Experience building customer-facing technical solutions or integrations. Experience creating reusable developer tools, frameworks, or open-source contributions. Consulting, Solutions Engineering, or Technical Customer Success experience in enterprise environments.

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

Skills for this role

Software EngineeringAPIDistributed SystemsCloud PlatformsBackend DevelopmentGenerative AILLMMachine LearningKubernetesCloud InfrastructureObservabilityMLOpsSolutions Engineering

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

Scalable AI Architecture Design
The role requires designing robust, production-grade AI architectures for Fortune 100 enterprises. You must be able to translate complex business requirements into scalable technical solutions that integrate seamlessly with existing enterprise infrastructure.
Customer-Facing Technical Leadership
You will act as the primary technical bridge between Cisco and its enterprise clients. This involves leading workshops, guiding customers through deployment lifecycles, and troubleshooting production issues while maintaining high-level communication.
Product Feedback Loop Integration
A core priority is synthesizing real-world customer deployment challenges into actionable feedback for internal Product and Engineering teams to drive platform enhancements and build reusable tools.

Likely challenges to think through

Balancing Customization with Scalability
Forward deployed roles often face pressure to build bespoke solutions for specific clients. The challenge is to solve immediate customer problems while ensuring the resulting code, tools, or best practices are reusable and scalable for future deployments.
Navigating High-Stakes Ambiguity
Working with cutting-edge AI systems in Fortune 100 environments often involves undefined technical constraints or evolving requirements. You must be prepared to maintain stability and progress in fast-paced, high-pressure scenarios where the path to production is not always clear.

Practice your answers

Technical practice

Designing Scalable AI Architectures

What to coverExplain your approach to designing AI systems that must scale for Fortune 100 enterprise needs. Focus on trade-offs between latency, throughput, and cost, and how you ensure the architecture remains maintainable in production environments.

Productionizing Generative AI and LLMs

What to coverDescribe the challenges of moving LLM applications from prototype to production. Discuss strategies for handling model inference, prompt management, and ensuring reliability or accuracy in enterprise-grade deployments.

Troubleshooting Distributed Systems

What to coverDetail your methodology for diagnosing complex issues in distributed cloud environments. Mention specific tools or techniques you use for observability, such as distributed tracing, logging, or monitoring metrics to isolate bottlenecks.

MLOps and Lifecycle Management

What to coverDiscuss the importance of MLOps in maintaining AI systems. Explain how you manage model versioning, automated testing, and continuous deployment pipelines to ensure consistent performance over time.

Integrating AI with Enterprise Infrastructure

What to coverExplain how you approach building integrations between AI models and existing enterprise backend systems. Address security, data privacy, and API design considerations when connecting new AI tools to legacy infrastructure.

Kubernetes for AI Workloads

What to coverDescribe your experience using Kubernetes to manage AI or machine learning workloads. Focus on how you handle resource orchestration, scaling, and containerization for high-demand production services.

Developing Reusable Developer Tools

What to coverExplain your process for identifying common pain points in customer deployments and turning those into reusable frameworks or tools. Discuss how you balance building custom solutions for one client versus creating assets that benefit future projects.

Cloud Platform Optimization

What to coverDiscuss how you optimize cloud infrastructure for AI applications. Address considerations like cost management, resource allocation, and selecting the right cloud services to meet specific performance requirements.

Behavioural practice

Managing Ambiguity in Customer Engagements

What to coverDescribe a time you worked in a fast-paced, ambiguous environment with an enterprise client. Explain how you clarified technical requirements and business goals to deliver a successful outcome.

Influencing Product Strategy via Customer Feedback

What to coverProvide an example of how you translated real-world customer feedback into actionable product improvements. Detail how you collaborated with internal Product and Engineering teams to ensure the platform evolved to meet market needs.

Leading Technical Workshops and Architecture Discussions

What to coverShare an experience where you led a technical workshop for a client. Explain how you communicated complex concepts to stakeholders and reached a consensus on the proposed architecture.

Collaborating Across Cross-Functional Teams

What to coverDescribe a situation where you had to bridge the gap between customer needs and internal engineering constraints. Explain the actions you took to align both parties and the final result of that collaboration.

Sharpen your knowledge

Topics to prepare

AI Production Architecture
Review the lifecycle of deploying Generative AI and LLM applications into enterprise environments. Exercise: Draft a high-level architecture diagram for a RAG (Retrieval-Augmented Generation) system, focusing on scalability, latency, and security. This is relevant as the role requires designing scalable AI solutions for Fortune 100 clients.
Distributed Systems Troubleshooting
Study common failure modes in distributed systems, such as network partitions, consistency issues, and service discovery failures. Exercise: Simulate a scenario where a microservice integration fails under high load and outline a step-by-step debugging process. This is relevant to the requirement for troubleshooting complex production issues across the technology stack.

Skills to revise

Kubernetes and MLOps
Practice deploying containerized applications and managing model lifecycles. Exercise: Create a simple CI/CD pipeline that automates the deployment of a containerized Python service to a Kubernetes cluster. This is relevant as the role prefers experience with Kubernetes and MLOps for production AI deployments.
Technical Consulting and Communication
Refine the ability to translate complex technical concepts for non-technical stakeholders. Exercise: Prepare a 5-minute presentation explaining a technical trade-off (e.g., choosing between different LLM hosting strategies) to a hypothetical business stakeholder. This is relevant to the role's focus on leading workshops and partnering with enterprise customers.
API Design and Integration
Focus on building robust, reusable APIs for enterprise-grade software. Exercise: Design a RESTful API specification for a tool that integrates an external AI model with an internal enterprise database. This is relevant to the requirement for building integrations and reusable tools that accelerate customer success.

Your preparation checklist

Map your production-grade software experience
Prepare a summary of your 7+ years of software engineering, specifically highlighting complex backend systems, distributed architectures, and API integrations. Focus on projects where you owned the full lifecycle from design to production deployment.
Develop an AI/ML deployment case study
Create a structured walkthrough of a production AI or LLM project you supported. Emphasize how you handled scalability, MLOps, or observability challenges, and explain how your technical decisions directly impacted the end-user or business outcome.
Prepare for customer-facing technical scenarios
Practice explaining complex technical architectures to non-technical stakeholders or enterprise clients. Prepare examples of how you have led technical workshops or navigated ambiguous requirements to deliver a successful solution.
Review Kubernetes and cloud infrastructure expertise
Be ready to discuss your hands-on experience with Kubernetes, cloud-native tools, and infrastructure management. Prepare to explain how you troubleshoot production issues across these layers in a high-stakes enterprise environment.
Formulate questions about the FDE-Product feedback loop
Prepare thoughtful questions for the interviewers regarding how the FDE team influences the product roadmap. Ask about specific processes for sharing customer feedback with internal engineering teams to demonstrate your interest in product innovation.

Availability has not yet been verified. 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…