Senior Backend Engineer (AI-Driven Developer)

Location: Cape Town (Hybrid – in-office Mon–Wed / Remote)

Reports to: Senior Director, Engineering

About NYSHEX.

NYSHEX is transforming global shipping logistics by uniting carriers, shippers, and NVOCCs through a shared digital infrastructure that ensures contract integrity and operational efficiency. As we scale our platform, we are increasingly embracing AI to accelerate backend performance,

automate reliability, and embed intelligence throughout our services. We’re looking for backend engineers who thrive at the intersection of system design and cutting-edge AI productivity.

Role Summary

As a Senior Backend Engineer at NYSHEX, you’ll play a key role in designing, developing, and optimizing our platform’s backend services. You will build resilient APIs, integrate AI-powered tooling across the SDLC, and work closely with cross-functional teams to drive a next-generation

developer and customer experience. You'll champion clean architecture, observability, and secure design—while experimenting with generative AI to accelerate delivery and elevate code quality.

Key Responsibilities

Backend Development & Architecture

• Build, maintain and refactor robust, scalable APIs and services using Java, Spring, Node.js, or Python.

• Architect event-driven and microservices-based systems with reliability, observability, and future-proofing in mind.

• Integrate generative AI tooling (e.g., GitHub Copilot, CodeWhisperer) to accelerate development, prototyping, and refactoring.

• Collaborate with front-end and data teams to expose data pipelines and model inferences via performant service interfaces.

AI-Augmented Engineering & Productivity

• Use GenAI tools to auto-generate unit/integration tests, API docs, and boilerplate code.

• Leverage LLM-based assistants for bug localization, refactoring suggestions, and database query optimization.

• Experiment with AI-enhanced CI/CD workflows (e.g., pull request summaries, release note generation, semantic code diff analysis).

• Maintain code health and performance using AI-integrated linters andstatic analysis tools.

Intelligent Observability & Reliability

• Instrument backend services using DataDog, CloudWatch, and OpenTelemetry—enhanced with AI-driven anomaly detection.

• Build and maintain dashboards for ML-backed metrics like predicted error spikes or latency regressions.

• Participate in on-call rotations and use LLMs to auto-draft incident summaries and recommend remediation based on past patterns.

Security, Compliance & Quality Assurance

• Ensure backend systems are secure and compliant by incorporating AI-enhanced static (SAST) and dynamic (DAST) analysis.

• Implement secure-by-default patterns and automate threat modeling using tools like OWASP Threat Dragon + AI summarization.

• Validate AI-generated code using formal review guidelines for performance, security, and maintainability.

Cross-Functional Collaboration

• Translate product requirements into technical specs through collaboration with Product and Design.

• Partner with data science teams to operationalize models and integrate AI inference APIs securely.

• Help shape developer experience tooling—automating repetitive backend tasks via LLM-powered scripts or assistants.

Technical Leadership & Mentorship

• Lead architectural discussions, contribute to ADRs, and prototype AI-

• enhanced service patterns (e.g., auto-tuned throttling, intelligent retries).

• Mentor mid-level engineers in backend best practices and the responsible use of AI in software development.

• Conduct code reviews with an emphasis on generative code validation, observability integration, and system modularity.

AI-Driven Backend Engineering Stack

Languages & Frameworks

• Java (Spring Boot), Node.js (Express), Python (FastAPI or Flask)

• Kafka, RESTful APIs, GraphQL

• PostgreSQL, Aurora, Redis

AI Productivity Tools

• GitHub Copilot, Amazon CodeWhisperer, Cursor, ChatGPT CLI

• Cursor, DeepSource, CodeQL, SonarQube with GenAI rule suggestion

• LLM-based prompt libraries for test generation and PR assistance

Observability & Infrastructure

• AWS (EC2, S3, ECS, Lambda, CloudWatch), Kubernetes, Docker

• Datadog, OpenTelemetry, New Relic with AI-powered alert correlation

• Terraform, GitHub Actions with GenAI-enhanced pipeline steps

Security & Compliance

• SonarQube, Snyk or Burp Suite, and AWS Inspector or Black Duck for AI-scanned vulnerabilities

• Secret scanning and encryption analysis using AI-recommended policies

• GDPR/CCPA considerations for data passed through AI systems

Cloud & Infrastructure

• Cloud Services: Proficient with AWS services (S3, SES, SNS, SQS, Kafka,

• Step Functions, CloudFront, Lambda, etc.) to serve static assets and edge functions.

• AI Agents: AWS Bedrock, Anthropic Claude, or OpenAI endpoints—experience deploying and securing model endpoints is a plus.

• Data Fetch: RESTful APIs or GraphQL (Apollo Client or Relay); use SWR or React-Query for data fetching and caching.

Qualifications

Required

• 6+ years of backend or full-stack software engineering experience

• Demonstrated experience building high-availability services in cloud-native environments (preferably AWS)

• Deep understanding of distributed systems, containerization (Kubernetes), and CI/CD best practices

• Hands-on experience with GenAI tools like GitHub Copilot or AI-enhanced testing/documentation frameworks

• Strong written/verbal communication and cross-functional collaboration skills

Preferred

• Prior experience integrating LLM-powered APIs or embedding AI inference into services

• Experience with serverless patterns or streaming systems (Kafka, Kinesis)

• Familiarity with ML observability and operationalizing model endpoints

• Exposure to prompt engineering or maintaining dynamic prompt templates in a backend context

Performance Metrics (KPIs)

• Code Coverage: ≥ 85% across backend services

• GenAI Usage: ≥ 50% of commits or PRs incorporate AI-generated code/tests

• Incident Response: Draft 90%+ of post-mortems with LLM tooling; mean time to resolution reduced by 30%

• System Reliability: Maintain ≥ 99.95% uptime across key backend services

• Mentorship: Lead 1+ internal AI tool workshop or backend learning session per quarter

What We Offer

• Unlimited PTO and hybrid flexibility

• Comprehensive benefits supporting mental, physical, and financial wellness

• Annual offsites to recharge, connect, and innovate

• Continuous learning with support for AI/DevOps conferences and workshops

• A forward-looking engineering culture ready to pioneer AI-powered development practices