Senior AI Engineer – Java & Generative AI

Intellibus designs and engineers technology platforms for established FinTech and financial-services firms. We build production-grade systems that help organizations solve complex business problems through software engineering, data, and emerging technologies.

We are looking for engineers who can turn AI capabilities into practical, reliable applications that deliver measurable business value.

The AI Engineer Role

Hands-on Senior AI Engineer with 12+ years of software engineering experience and strong expertise in Core Java, Spring Boot, enterprise backend development, microservices, Microsoft Azure, and Large Language Models (LLMs).

This is a senior engineering position requiring someone who has spent years designing, developing, and deploying complex enterprise applications and has subsequently gained practical experience integrating AI and LLM capabilities into production systems.

The ideal candidate is first and foremost a strong software engineer who understands enterprise application architecture, backend engineering, distributed systems, and cloud-native development, with substantial hands-on experience building AI-powered applications.

You will be responsible for designing and developing enterprise AI solutions, integrating LLMs into existing applications, building intelligent workflows, and delivering production-ready systems within complex financial-services environments.

Key Responsibilities

1. Core Java & Enterprise Backend Engineering

• Demonstrate advanced hands-on expertise in Core Java, including OOP, Collections, Streams API, Multithreading, Concurrency, JVM fundamentals, and performance optimization.
• Design, develop, and maintain enterprise-grade backend applications using Java and Spring Boot.
• Build scalable RESTful APIs, microservices, and distributed applications.
• Implement complex business logic, enterprise integrations, asynchronous processing, and event-driven architectures.
• Work with Spring Security, Spring Data, Hibernate, JPA, and related frameworks.
• Apply appropriate design patterns, coding standards, and architectural principles.
• Troubleshoot complex production issues, optimize application performance, and improve system reliability.

2. Microservices & Distributed Systems

• Design and implement enterprise microservices architectures.
• Develop resilient, scalable, and loosely coupled services.
• Work with messaging and event-streaming technologies such as Apache Kafka.
• Implement service-to-service communication, API integrations, and asynchronous workflows.
• Understand distributed transactions, fault tolerance, circuit breakers, and high-availability architectures.
• Work with relational and NoSQL databases and optimize data access patterns.

3. AI & Large Language Model Engineering

• Design, build, and integrate LLM-powered applications into enterprise platforms.
• Work hands-on with commercial and open-source LLMs, including Azure OpenAI and other model providers.
• Develop LLM integrations using APIs, SDKs, and backend services.
• Build AI-powered workflows, intelligent automation, and agentic applications.
• Implement Retrieval-Augmented Generation (RAG) architectures.
• Work with embeddings, vector databases, semantic search, and enterprise knowledge retrieval.
• Develop prompt engineering strategies, structured outputs, function calling, and tool integrations.
• Implement LLM orchestration, context management, conversation memory, and multi-step AI workflows.
• Evaluate model responses for accuracy, relevance, hallucinations, and consistency.
• Implement guardrails, validation, security controls, and responsible AI practices.
• Optimize latency, token consumption, model performance, and inference costs.
• Take AI applications beyond prototypes and deliver reliable, scalable production solutions.

4. Microsoft Azure & Cloud Engineering

• Design and deploy enterprise applications and AI solutions on Microsoft Azure.
• Work with Azure OpenAI Service, Azure AI services, Azure App Services, Azure Functions, and related cloud services.
• Integrate AI services with enterprise applications, APIs, and data platforms.
• Work with Azure cloud architecture, networking, security, identity, and access management.
• Build and manage CI/CD pipelines and automated deployment workflows.
• Apply cloud-native engineering principles, scalability, monitoring, and reliability practices.

5. Python, Data Engineering & Enterprise Platforms

• De…