Description

As a Forward Deployed Engineer (FDE) at QBurst, you will occupy one of the most strategically important engineering roles within the organization. Working directly with high-value clients, you will embed within their engineering teams to design, build, and deliver impactful technology solutions. You will combine deep technical expertise with strong stakeholder management skills to solve complex business challenges, accelerate delivery, and establish trusted client relationships.

Success in this role means rapidly understanding client needs, translating them into scalable technical solutions, delivering high-quality outcomes, and creating opportunities for long-term strategic partnerships through technical excellence and business impact.

Responsibilities

- Embed within client engineering teams and contribute directly to the design, development, deployment, and support of production-grade applications and AI-powered solutions
- Drive end-to-end software delivery, from requirements gathering and solution design to implementation, testing, deployment, and post-production support
- Operate as a senior engineering resource within client environments, ensuring high-quality and timely execution
- Leverage AI-assisted development tools such as Claude Code, Cursor, OpenAI Codex, GitHub Copilot, or equivalent platforms as an integral part of the software development lifecycle
- Develop and promote reusable prompt engineering practices, AI workflows, and productivity-enhancing development patterns
- Evaluate AI tools and provide recommendations on their effective usage, limitations, and validation requirements
- Serve as a trusted technical advisor by providing guidance on architecture, scalability, security, performance, and engineering best practices
- Translate business requirements into practical technical solutions and lead technical design discussions and architecture reviews
- Establish and uphold coding standards, quality assurance practices, and engineering excellence across engagements
- Build strong relationships with client stakeholders, including engineering leaders, product teams, and executive sponsors
- Identify opportunities to expand client engagements by uncovering additional business needs and recommending value-driven solutions
- Support technical presentations, solution discussions, and strategic conversations that contribute to account growth
- Contribute reusable frameworks, accelerators, AI workflows, prompt libraries, and best practices to QBurst’s internal knowledge repository
- Mentor team members and promote the adoption of modern engineering and AI-enabled development practices
- Stay current with emerging technologies, AI advancements, and industry trends to continuously enhance delivery capabilities

Requirements





- 5+ years of hands-on software engineering experience delivering enterprise-grade applications in production environments
- Proven experience delivering at least two complete software development lifecycles, from solution design through deployment and ongoing support
- Demonstrated daily use of AI-assisted development tools such as Claude Code, GitHub Copilot, Cursor, Codex, or equivalent platforms in a professional setting
- Proficiency in at least two programming languages, with expertise spanning backend, frontend, or data engineering technologies
- Hands-on experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform, including deployment, configuration, monitoring, and troubleshooting
- Strong understanding of APIs, microservices architectures, CI/CD pipelines, and modern software development methodologies
- Excellent communication and presentation skills, with the ability to explain complex technical concepts to both technical and non-technical audiences, including executive stakeholders
- Proven ability to manage client expectations, navigate critical incidents, and maintain stakeholder trust in demanding environments
- Strong consulting mindset with the ability to balance technical excellence and business outcomes
- Prior experience as a Forward Deployed Engineer, Solutions Engineer, Technical Consultant, or in a client-facing engineering role is preferred
- Experience building AI agents and workflows using frameworks such as LangChain, LangGraph, CrewAI, AutoGen, or similar technologies is desirable
- Professional certifications such as AWS Skilled, Azure Solutions Architect Expert, TOGAF, or AI/ML specialty certifications are a plus
- Exposure to pre-sales activities, including solution discovery, estimation, proposal development, and technical presentations, is advantageous

Update as on 7th Aug- Please consider below

Role Brief: Forward Deployed Engineer (FDE)

Experience

8+ years in software engineering, with at least 1 to 2 years of genuine LLM/GenAI project work.

We are hiring Forward Deployed Engineers to work directly with our clients on AI-driven solution delivery. This is a client-facing engineering role, not a backend seat. The right candidate combines strong technical execution with the ability to own outcomes in front of stakeholders.

What We Look For

- Client-Facing Ownership

Comfortable presenting to clients,



running discovery conversations, translating business problems into technical solutions, and owning delivery end to end.

- LLM-Native AI Depth

Hands-on experience designing and shipping LLM-powered systems in production, not just ML theory or POC. This means architecting RAG pipelines end to end, building agentic workflows with tool use and multi-step orchestration, writing and running evals to measure quality, and making deliberate model choices (model selection, routing, context management, cost and latency tradeoffs). Should understand failure modes of LLM systems (hallucination, prompt injection, degraded retrieval) and how to engineer around them.

- Full-Stack Capability

We need engineers who can ship a working product on their own, not specialists who cover one layer and wait on others.

- Backend: Strong in one or more of Java, Golang, Python, Node.js, or .NET. Able to design clean REST APIs, model data sensibly, handle auth, and reason about performance and error handling in production.
- Frontend: Able to build functional, presentable UIs in React, Angular, Vue.js, or similar.
- Prototyping speed: Able to go from a whiteboard conversation to a working proof of concept in days, then harden it into something production-ready. Candidates who only operate inside mature codebases with established scaffolding tend to struggle in this role.
- AI integration across the stack: Able to wire LLM calls, streaming responses, and tool use into both backend services and frontend experiences, not just notebooks.
- Data and Infrastructure

We are not hiring platform or DevOps engineers, but FDEs must be self-sufficient on infra.

- Databases: Fluent in SQL (Postgres or MySQL) plus at least one NoSQL store. Vector database exposure (pgvector, Pinecone, Qdrant) is a strong plus. Experience building knowledge bases for retrieval (chunking, embedding, indexing) and working with knowledge graphs (Neo4j or similar, graph-based RAG) is highly valued.
- Cloud and containers: Hands-on deployment on at least one major cloud (AWS, Azure,

or GCP). Docker is a must; Kubernetes is a plus.

- CI/CD and observability: Comfortable setting up pipelines, managing environments and secrets, and maintaining logging and monitoring hygiene without a separate DevOps engineer.
- Security awareness: Sensible defaults around auth, API keys, data handling, and client data isolation. Security practices in AI/GenAI systems are especially important: prompt injection defenses, guardrails on model outputs, and safe handling of client data in LLM pipelines.
- Ambiguity Tolerance

Thrives with loosely defined problems, shifting requirements, and minimal hand-holding. Can scope, decide, and move without waiting for detailed specs.

📌 Forward Deployed Engineer (Bengaluru)
🏢 QBurst
📍 Bengaluru