Forward Deployed Engineer (FDE)

RED ALPHA CYBERSECURITY PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

RED ALPHA CYBERSECURITY PTE. LTD. seeks a senior engineering-focused professional to own technical discovery, design and implement production software across APIs, data pipelines, and AI-enabled workflows.

You will integrate with enterprise environments, deploy with CI/CD, and provide repeatable handoffs and documentation. The role emphasizes strong Python skills, backend fundamentals, and production experience with containers, cloud infra, and IaC, while maintaining excellent customer-facing

Qualifications

• Hands-on software engineering across multiple domains (backend/platform/data).
• Strong Python and backend fundamentals with API design.
• Experience deploying to cloud environments and containers.

Responsibilities

• Lead technical discovery with customers to understand workflows and acceptance criteria.
• Design and build production software across APIs, databases, data pipelines, and AI-enabled workflows.
• Deploy securely with CI/CD, IaC, and cloud infrastructure; monitor and roll back if needed.
• Run architecture reviews, demos, and handoffs; surface risks early for customer success.
• Maintain operator runbooks, docs, and clear architectural diagrams for customers.

Skills

Python

Backend fundamentals

APIs

Testing

Production debugging

Cloud concepts

Customer-facing

Education

No degree requirement

Tools

Docker

Kubernetes

Terraform

CI/CD

Observability

Job description

The role in one sentence

You will sit with customers, find the real problem behind the request, design the solution, build the critical pieces, deploy it into their environment, and stay accountable until it is reliable, understood, and handed off.

What you will own

• Lead technical discovery with customers: understand workflows, constraints, data, security boundaries, acceptance criteria, and what success actually means.
• Design and build production software across APIs, backend services, databases, data pipelines, cloud/container infrastructure, and AI-enabled workflows.
• Integrate with imperfect enterprise environments where access is constrained, ownership is unclear, and requirements change once the system meets reality.
• Make sound architecture tradeoffs across relational databases, warehouses/lakes, search/vector stores, deployment targets, authentication models, and integration patterns.
• Deploy securely and repeatably using containers, CI/CD, cloud infrastructure, IAM/networking, monitoring, rollback plans, and infrastructure-as-code where appropriate.
• Build and evaluate enterprise AI systems using RAG, tools, MCP-style integrations, governed data access, test sets, monitoring, and human-review controls when the use case calls for it.
• Run demos, architecture reviews, incident calls, and executive/technical updates. Surface risks early and separate facts, assumptions, and decisions.
• Drive the final mile: hardening, documentation, operator runbooks, architecture diagrams, API/data docs, known limitations, and a handoff the customer can actually use.
• Raise the bar for delivery discipline across the team through code review, technical judgment, clear decision-making, and calm execution under pressure.

What we are looking for

• 4-6 years of hands-on experience across software engineering, backend/platform engineering, data engineering, cloud/infrastructure, solutionsengineering, or similar roles.
• No degree requirement. We care about evidence that you can build, reason, communicate, and deliver in production environments.
• Strong Python and backend fundamentals, including APIs, authentication/authorization, testing, error handling, integration patterns, and production debugging.
• Practical data experience: SQL, relational modeling, migrations, ingestion/ETL/ELT, data quality, orchestration, and the trade-offs between different storage/serving patterns.
• Production deployment experience with Docker and cloud environments; familiarity with Kubernetes, IAM/networking, CI/CD, observability, and Terraform or comparable IaC is strongly preferred.
• Enough AI/LLM systems knowledge to reason about RAG, tools/agents, evaluations, model limitations, data access, monitoring, and responsible deployment - not just prompt demos.
• Excellent customer-facing instincts. You can run a technical conversation, challenge assumptions respectfully, explain tradeoffs, and keep trust when something is broken.
• High ownership with good boundaries: you move quickly, but you respect customer environments, least-privilege access, change control, auditability, and operational safety.

The people who thrive in this role

• Prefer a working deployment over a beautiful slide deck - but can produce the slide deck when the room needs one.
• Enjoy switching levels: one hour debugging TLS or a bad query plan, the next explaining scope tradeoffs to a customer lead.
• Do not need perfect requirements to start, but know how to turn ambiguity into explicit assumptions, acceptance criteria, and a plan.
• Care about the handoff as much as the build. A solution is not finished if nobody can operate, explain, or recover it.

What good looks like after 12 months

• You can independently lead a customer problem from discovery through architecture, implementation, deployment, demo, and handoff.
• You consistently choose simple, reliable designs and can explain why they are the right trade-off for the customer environment.
• You have become a trusted technical counterpart for customers and an escalation point for complex delivery issues internally.
• The systems you ship survive contact with production: they are observable, tested, documented, recoverable, and owned.