Senior Backend Engineer – AI‑Driven Development Lifecycle

About the role

We are seeking a Senior Backend Engineer to lead the design and implementation of an agentic, AI‑driven software development lifecycle (AI‑DLC) for a regulated banking environment. You will work on cutting‑edge LLM orchestration, RAG pipelines, and high‑performance microservices built on modern Java and Spring technologies.

Key responsibilities

• Architect and develop scalable backend services using Java 17+, Spring Boot 3.x, and Spring Cloud microservice patterns (API Gateway, Circuit Breaker, Saga, CQRS, Outbox).
• Design and operate LLM agent orchestration frameworks (LangChain4j, Spring AI, AutoGen, CrewAI) and integrate them with enterprise tooling.
• Build and optimise RAG pipelines, including document ingestion, chunking, embedding models, and vector database management.
• Implement messaging and caching solutions with Apache Kafka, Redis, and relational databases (SQL Server, Oracle, PostgreSQL).
• Deploy and manage cloud infrastructure on AWS (ECS/EKS, Lambda, RDS, etc.) using IaC (CDK, CloudFormation, Terraform) and CI/CD pipelines (GitHub Actions, Docker, Kubernetes).
• Ensure security, compliance, and banking‑specific data handling (OAuth2/OIDC, PCI‑DSS, UAE PDPL, CBUAE residency).
• Provide observability through OpenTelemetry, Prometheus, Grafana, and structured logging.

Required profile

• 10+ years of hands‑on backend engineering experience in production environments.
• Proven experience delivering enterprise‑scale AI‑DLC or agentic SDLC implementations.
• Background in banking, fintech, or other highly regulated domains is strongly preferred.
• Degree in Computer Science or related field, or equivalent professional experience.

Required skills

• Java 17+, records, sealed classes, virtual threads (Project Loom)
• Spring Boot 3.x, Spring Security, Spring Data JPA/JDBC, Spring WebFlux, Spring Cloud
• API design with OpenAPI 3.x, RESTful services
• LLM orchestration frameworks: LangChain4j, Spring AI, AutoGen, CrewAI
• RAG pipeline components, prompt engineering, vector databases (pgvector, Weaviate, Qdrant)
• Apache Kafka (topic design, Schema Registry), Redis (cluster, Lua scripting)
• SQL Server, Oracle, PostgreSQL
• AWS services (ECS/EKS, Lambda, RDS, S3, SQS/SNS, IAM)
• Infrastructure‑as‑Code: AWS CDK, CloudFormation, Terraform
• CI/CD: GitHub Actions, Docker, Kubernetes
• Security protocols: OAuth2, OpenID Connect, JWT, PCI‑DSS, data masking/tokenisation
• Observability tools: OpenTelemetry, Prometheus, Grafana