Forward Deployment Engineer

Role purpose

• The FDE Supporting Engineer is a hands-on supporting engineer who turns the engagement’s target architecture into working, verified software. This is not an entry-level coding role. The person brings strong full-stack execution and AI-assisted or agentic engineering capability, owns defined workstreams, and works closely with the FDE Lead to deliver production-minded outcomes while developing broader architecture and client leadership skills.

Core qualifications

• Strong hands-on software engineering experience with evidence of building and shipping end-to-end application capabilities.
• Full-stack depth across modern frontend, backend, API, data, and cloud components, with the ability to explain implementation decisions beyond a single layer.
• Practical experience using AI-assisted development tools, LLM APIs, or agent frameworks to build working artifacts rather than only exploratory demonstrations.
• Understanding of system design, integration, cloud deployment, security, testing, and operational concerns sufficient to execute effectively within a target architecture.
• Ability to own a defined delivery scope, work with limited supervision, communicate constraints honestly, and collaborate closely with Lead engineers and stakeholders.
• Strong debugging, testing, verification, and problem-solving skills, with attention to maintainability and evidence of quality.
• Clear written and verbal communication for engineering collaboration and technical working sessions.

Technical capabilities

• Application engineering: hands-on capability in one or more of Java, Python, .NET, JavaScript/TypeScript, Node.js, React, Angular, or comparable technologies.
• APIs and integration: REST or GraphQL, microservices, event-driven patterns, database integration, and distributed application fundamentals.
• Cloud and delivery: Azure, AWS, or GCP; containers; CI/CD; Git; Infrastructure as Code; observability; and automated testing or deployment practices.
• AI engineering: LLM APIs, prompt engineering, RAG, vector databases or search, evaluation and verification, and frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, or AutoGen.
• Data: SQL and modern data concepts, ETL or ELT, streaming or analytics exposure, and the data flows required to support applications and AI use cases.
• Security: secure coding, secrets handling, authentication and authorization, encryption basics, dependency hygiene, and responsible AI practices.