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Java AI Engineer

Artech
4 hours ago
Full-time
On-site
Phoenix, Arizona, United States
Requisition ID: 105030-1 Title: Java AI Engineer Duration: 6-12 months with possible extension Location: Phoenix, AZ (Local Candidate only) Salary Range: $40 -$45 an hour on W2/C2C

Job Description: Java & Backend Development • Design and develop scalable applications using Java 17/21, Spring Boot, Spring Cloud, and Microservices architecture. • Develop RESTful APIs and event-driven services using Kafka. • Implement secure applications using OAuth2, JWT, RBAC, and enterprise security standards. • Optimize performance, resiliency, and scalability of distributed systems. AI & Generative AI Solutions • Build and deploy AI-powered applications using OpenAI, Azure OpenAI, Claude, or similar LLM platforms. • Design Retrieval Augmented Generation (RAG) solutions using Vector Databases. • Develop AI Agents with tool calling, orchestration, memory management, and workflow automation. • Implement prompt engineering, semantic search, embeddings, and context management. • Integrate enterprise knowledge repositories with AI solutions. • Develop responsible AI guardrails, validation, and observability frameworks. Cloud & DevOps • Deploy applications on AWS, Azure, or GCP. • Build CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI. • Containerize services using Docker and Kubernetes. • Monitor production systems using Splunk, Grafana, Prometheus, ELK, or Cloud Monitoring solutions.

SKILLS_REQUIRED "Core Java • Java 17/21 • Spring Boot • Spring Cloud • Hibernate/JPA • REST APIs • Microservices • Kafka • SQL & NoSQL Databases AI / GenAI • LLM Integration • OpenAI / Azure OpenAI APIs • Prompt Engineering • RAG Architecture • AI Agents • LangChain / LlamaIndex • Embeddings & Semantic Search • Vector Databases (Pinecone, Chroma, Weaviate, Milvus) • MCP (Model Context Protocol) • Function Calling & Tool Usage Cloud & DevOps • AWS / Azure / GCP • Docker • Kubernetes • CI/CD Pipelines • GitHub Actions / Jenkins • Terraform (Preferred) Databases • PostgreSQL • MongoDB • DynamoDB • Redis • Snowflake Preferred • NVIDIA NIM • Agentic AI Frameworks • MLOps / LLMOps • FastAPI or Python • GraphRAG • Knowledge Graphs • Multi-Agent Systems"

ESSENTIAL_SKILLS "Core Java • Java 17/21 • Spring Boot • Spring Cloud • Hibernate/JPA • REST APIs • Microservices • Kafka • SQL & NoSQL Databases AI / GenAI • LLM Integration • OpenAI / Azure OpenAI APIs • Prompt Engineering • RAG Architecture • AI Agents • LangChain / LlamaIndex • Embeddings & Semantic Search • Vector Databases (Pinecone, Chroma, Weaviate, Milvus) • MCP (Model Context Protocol) • Function Calling & Tool Usage Cloud & DevOps • AWS / Azure / GCP • Docker • Kubernetes • CI/CD Pipelines • GitHub Actions / Jenkins • Terraform (Preferred) Databases • PostgreSQL • MongoDB • DynamoDB • Redis • Snowflake Preferred • NVIDIA NIM • Agentic AI Frameworks • MLOps / LLMOps • FastAPI or Python • GraphRAG • Knowledge Graphs • Multi-Agent Systems"

DESIRABLE_SKILLS Java & Backend Development • Design and develop scalable applications using Java 17/21, Spring Boot, Spring Cloud, and Microservices architecture. • Develop RESTful APIs and event-driven services using Kafka. • Implement secure applications using OAuth2, JWT, RBAC, and enterprise security standards. • Optimize performance, resiliency, and scalability of distributed systems. AI & Generative AI Solutions • Build and deploy AI-powered applications using OpenAI, Azure OpenAI, Claude, or similar LLM platforms. • Design Retrieval Augmented Generation (RAG) solutions using Vector Databases. • Develop AI Agents with tool calling, orchestration, memory management, and workflow automation. • Implement prompt engineering, semantic search, embeddings, and context management. • Integrate enterprise knowledge repositories with AI solutions. • Develop responsible AI guardrails, validation, and observability frameworks. Cloud & DevOps • Deploy applications on AWS, Azure, or GCP. • Build CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI. • Containerize services using Docker and Kubernetes. • Monitor production systems using Splunk, Grafana, Prometheus, ELK, or Cloud Monitoring solutions.

Appreciate your quick response and please feel free to reach me out for any query you may have.

Thanks