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About the role
Core Experience: 3–5 years of professional software engineering with demonstrated ownership in production systems; experience in transactional, high-availability, retail, payment, or forecourt environments is a strong plus.
Go Stack: Strong proficiency in Go (1.20+), including gorilla/mux, MongoDB driver, JWT/OAuth 2.0, goroutines and channel-based concurrency, interface-driven design, and Go module management.
Payment & Forecourt Protocols: Familiarity with ISO 8583, EMV, NFC, and PCI-DSS compliance; familiarity with forecourt integration (IFSF, OPT, pump authorization) and serial/TCP communication protocols is recommended.
Agentic Systems Mastery: Proven hands-on experience building intelligent, automation-driven systems any platform, including agent-based workflow orchestration for multi-step transactions.
AI & Backend Integration: Hands-on experience integrating LLMs or AI services into backend systems, including prompt engineering, RAG, and automation workflows
Data & Vector Technologies: Familiarity with vector databases, embeddings, recommendation systems, and data pipelines for customer behavior analytics and product intelligence
Cloud & DevOps: Experience deploying Go applications to AWS with Docker multi-stage builds, Kubernetes Stateful Sets /Deployments, Helm chart management, and Jenkins CI/CD pipelines.
Web APIs & Real-Time Communication: Experience designing distributed microservice architectures with OAuth 2.0 M2M authentication, RESTful API design, inter-service coordination patterns, and event-driven processing for live transaction and forecourt operations.
Testing & Security: Strong test-driven development practices using Go's native testing package and testify; solid understanding of secure coding, PCI-DSS, precise currency arithmetic and protection against injection/tampering in high-value payment transactions.
Soft Skills: Strong communication and collaboration ability; growth mindset; effective cross-domain teamwork with initiative-taking and good judgment about when to seek guidance.
Candidates must demonstrate the ability to effectively leverage AI tools, automation, and modern AI technologies to enhance productivity, problem-solving, and innovation in their daily work. Proven ability to use AI-powered tools to improve efficiency, accelerate development, and solve complex problems. AI proficiency is required: ability to use and adapt to modern AI tools and technologies as part of day-to-day work.
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