Skip to main content
David Careers

Senior AI Engineer – Java & Generative AI

Posted today

intellibusNew York (NY)
Software DevelopersCustom Computer Programming Services

SENIORITY

Lead

SALARY

$75 - $85 / Hour

Apply (opens in a new tab)

About the role

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 RoleHands-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
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 EngineeringDesign, 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 EngineeringDesign 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
  • Develop Python applications, services, and automation supporting AI workflows.
  • Work with SQL, relational databases, and structured and unstructured data.
  • Build data-processing and integration workflows supporting LLM applications.
  • Work with PostgreSQL, Oracle, MySQL, or comparable enterprise databases.
  • Experience with Microsoft Fabric, data engineering, and enterprise analytics platforms is preferred.
  • Integrate enterprise data sources with AI models and retrieval systems.6. Engineering Quality & Production Delivery
  • Write clean, maintainable, testable, and production-quality code.
  • Conduct code reviews and mentor other engineers.
  • Develop unit, integration, and automated tests.
  • Troubleshoot complex application and infrastructure issues.
  • Implement application logging, monitoring, observability, and performance optimization.
  • Collaborate with architects, engineering leaders, and business stakeholders to deliver enterprise solutions.
  • Required Technical Qualifications 12+ years of professional software engineering experience.
  • Strong recent hands-on development experience in enterprise software engineering.
  • Proven experience designing and delivering complex production applications.
  • Practical experience building and deploying AI/LLM-powered applications.
Core Java & Backend: Advanced Core Java and Spring Boot expertise. Strong understanding of OOP, Collections, Streams, Multithreading, Concurrency, and JVM.Extensive experience with REST APIs, microservices, Spring Framework, Hibernate, and JPA.Strong understanding of enterprise application architecture and distributed systems. AI & LLMsDemonstrated hands-on experience integrating LLMs into enterprise applications. Experience with Azure OpenAI or comparable LLM platforms. Strong understanding of RAG, embeddings, vector databases, prompt engineering, and LLM orchestration. Experience building AI agents, tool calling, and intelligent workflows is highly desirable. Experience deploying and maintaining AI applications in production is strongly preferred. Azure & CloudStrong experience with Microsoft Azure and cloud-based application development. Experience integrating Azure AI services with enterprise applications. Familiarity with cloud security, identity, deployment, and monitoring.
Additional Skills: Working knowledge of Python. Strong SQL and database development experience. Exposure to Microsoft Fabric and enterprise data platforms. Experience with Kafka, Docker, Kubernetes, and CI/CD pipelines.
What We Are Looking For: We want engineers who: Have spent 12+ years building enterprise software applications. Are deeply hands-on with Java, Spring Boot, and backend engineering. Understand how large-scale enterprise applications are architected and implemented. Have moved beyond AI experimentation and have built actual LLM-powered applications. Can independently design, code, debug, and deploy complex solutions. Understand how to integrate AI capabilities into existing enterprise systems. Can take ownership of technical deliverables and work effectively with senior engineering teams.
Important: Candidates whose experience is primarily prompt engineering, Python scripting, AI tool usage, academic research, or AI-generated code without strong enterprise software engineering fundamentals will not be suitable for this position.
Compensation: $75 - $85 / HourOur Process
Initial Screening: 15-minute video call with the Recruiting Team. Assessments: One proctored GQ multiple-choice assessment and a SlideWare presentation (under 2 hours). Interview Rounds: Three 30-minute video interviews with the Technical Team, Project Lead, and Executive Team. Offer: Final decision and job offer. If this sounds like the kind of work you want to be doing, apply now. Our team will be in touch within the hour.

Before you apply

Applying takes about a minute. These four things decide how fast it moves after that.

Your profile is current

It's what we read first. Occupations, seniority and locations matter more than a long history.

Two examples you can talk through

Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.

A number in mind

What you're on now and what would make you move. We negotiate better when we know both.

Your notice period

Employers plan around it, and it's the question that stalls offers most often.

Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.

More like this