Java + AI Engineer

Stanley David and Associates

Chicago, IL, United States

3 months ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Experienced

Experience required

4 years minimum

Working hours

Regular working hours

Job source

dice.com

Tech stack

Java (Programming Language) Artificial Intelligence Amazon Web Services Microsoft Azure Cloud Computing Continuous Integration Information Engineering Machine Learning NoSQL Performance Tuning Search Technologies Software Deployment

+13 more

Software Engineering SQL Databases Management of Software Versions Google Cloud Enterprise Software Applications Large Language Models Prompt Engineering Generative AI Kubernetes Machine Learning Operations Restful APIs Docker Microservices

Job description

Design, develop, and deploy AI/ML and Generative AI solutions including LLM based applications, RAG pipelines, agents, and predictive models
Translate business use cases into production ready AI solutions with measurable outcomes
Deep knowledge in Java streams technology
Implement LLM orchestration, prompt engineering, vector search, embeddings, and model fine tuning
Develop scalable APIs and microservices to integrate AI capabilities into enterprise applications
Collaborate with Data Engineers, Data Scientists, Product Owners, and Cloud teams across onshore offshore models
Implement MLOps / LLMOps practices including CI/CD, monitoring, versioning, model governance, and observability
Ensure Responsible AI, security, compliance, and data privacy by design
Support production deployments, performance tuning, and continuous improvement of AI systems

Requirements

4-8+ years of experience in software engineering, ML engineering, or AI solution development
Strong proficiency in Java and experience
Hands on experience with Generative AI / LLMs, including RAG, embeddings, prompt engineering, and agents
Solid understanding of data engineering concepts, SQL/NoSQL, and feature pipelines
Experience deploying AI solutions on cloud platforms (Google Cloud Platform preferred; AWS/ Azure acceptable)
Familiarity with Docker, Kubernetes, and CI/CD pipelines
Strong problem solving, communication, and stakeholder collaboration skills