About this role

Core Backend Capabilities

Deep mastery of Java (Java 11 / 17 or later) and enterprise ecosystem development.

Advanced experience with Spring Boot, Spring Cloud, Spring Security, and Hibernate/JPA frameworks.

Proven expertise designing and scaling distributed systems, RESTful microservices, and high-volume transaction architectures.

Robust understanding of event-driven software architectures using Apache Kafka or RabbitMQ.

Strong relational database proficiency (Oracle, SQL Server) focusing on complex transactional consistency, ACID properties, and tuning.

AI / ML Integration Capabilities

Extensive experience serving and integrating AI/ML models in Java runtimes utilizing tools like LangChain4j, ONNX Runtime, or Deep Java Library (DJL).

Hands-on practice orchestrating interactions with Large Language Models (LLMs) via secure Enterprise APIs for text summarization, data extraction, or automated reasoning.

Familiarity with Vector Databases (such as pgvector, Pinecone, or Milvus) to support Retrieval-Augmented Generation (RAG) within financial applications.

Practical experience collaborating with Python-based ML engineering environments and operational frameworks (MLflow, Kubeflow) to transition model weights into high-performance Java APIs.

Familiarity with AI guardrails, model alignment testing, and architectural implementations that minimize hallucination or biases in transactional routing.

Cloud, DevOps & Tooling

Experience developing containerized deployments within enterprise cloud native infrastructure (Google Cloud Platform / Google Cloud Platform or Pivotal Cloud Foundry / PCF).

Proficiency managing infrastructure deployments via Docker and Kubernetes environments.

Expertise in continuous integration/delivery pipelines built using GitHub Actions, Bitbucket, or Bamboo.

Rigorous standard for testing, adhering strictly to Test-Driven Development (TDD) or Behavior-Driven Development (BDD) paradigms with JUnit and Mockito.

AI Java Engineer

Key skills

About this role