Phoenix, AZ · On-site
$52.25 - $67.25/hr
Building RAG pipelines utilizing Vector Databases (e.g., Pinecone, Milvus, pgvector, Qdrant). * Hands-on familiarity with MCP (Model Context Protocol) standards, tools, and agentic workflows.
Phoenix, AZ · On-site
$52.25 - $67.25/hr
Building RAG pipelines utilizing Vector Databases (e.g., Pinecone, Milvus, pgvector, Qdrant). * Hands-on familiarity with MCP (Model Context Protocol) standards, tools, and agentic workflows.
Phoenix, AZ · On-site
$110K - $125K/yr
Experience with Qdrant, Neo4j, or other vector/graph infrastructure * Experience supporting RAG, search, evaluation, or agent platforms * Experience in enterprise or regulated environments
Phoenix, AZ · On-site
$110K - $125K/yr
Experience with Qdrant, Neo4j, or other vector/graph infrastructure * Experience supporting RAG, search, evaluation, or agent platforms * Experience in enterprise or regulated environments
| Aspect | Qdrant | Data Scientist |
|---|---|---|
| Required Credentials | Technical certifications, knowledge of vector databases | Degree in Data Science, Statistics, or related field |
| Work Environment | Tech companies, startups, AI-focused firms | Research labs, tech companies, consulting firms |
| Industry Usage | AI, machine learning, data storage | Data analysis, predictive modeling, research |
Qdrant primarily focuses on managing and deploying vector similarity search databases, requiring technical skills in database management and AI tools. Data Scientists analyze data, build models, and interpret results. While both roles operate within the tech and AI industry, Qdrant specialists are more technical and infrastructure-oriented, whereas Data Scientists focus on data analysis and modeling.
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$52.25 - $67.25/hr
Other
Posted 13 days ago
Location: Phoenix, AZ (Onsite / Hybrid)
Employment Type: Contract
Job SummaryWe are looking for a highly skilled Lead Java Developer with expertise in Artificial Intelligence (AI), Generative AI (LLMs, RAG, MCP), and Full Stack Development to design and build next-generation enterprise applications. In this role, you will lead the architecture, engineering, and deployment of resilient, AI-powered enterprise microservices and customer-facing web applications.
The ideal candidate brings strong hands-on experience in Java core platform engineering, integration with modern AI architectures (Retrieval-Augmented Generation, Model Context Protocol, and Large Language Models), and modern frontend frameworks (React/Angular/Node.js).
Key ResponsibilitiesTechnical Leadership & Architecture: Lead the end-to-end design, development, and architectural delivery of scalable, secure Java-based backend microservices integrated with advanced AI/LLM capabilities.
AI & LLM Integration: Design and implement RAG (Retrieval-Augmented Generation) pipelines, integrate MCP (Model Context Protocol) servers/tools, vector databases, and enterprise LLM orchestration framework (e.g., Spring AI, LangChain4j, Semantic Kernel).
Full Stack Execution: Oversee and contribute to full stack development-connecting modern frontend frameworks (React.js, Angular, or Vue.js) with AI-enhanced RESTful and GraphQL APIs.
Enterprise Security & Governance: Implement enterprise-grade security, data protection, privacy controls, and API governance suitable for large-scale financial and payment systems.
Engineering Best Practices: Drive code reviews, automated unit/integration testing, CI/CD pipeline automation, observability/monitoring, and performance tuning across the cloud stack (AWS/Google Cloud Platform/Azure).
Mentorship & Collaboration: Guide and mentor software engineers, partner closely with Data Scientists, AI Engineers, Product Managers, and Solution Architects to translate business requirements into technical reality.
Education: Bachelor's or Master's degree in Computer Science, Software Engineering, or a related field.
Core Java Leadership: 8+ years of hands-on Java engineering experience (Java 17/21, Spring Boot, Spring Cloud, Microservices, Event-driven architecture with Kafka/RabbitMQ).
AI / GenAI Expertise: 2+ years of practical experience integrating AI capabilities into enterprise applications:
Building RAG pipelines utilizing Vector Databases (e.g., Pinecone, Milvus, pgvector, Qdrant).
Hands-on familiarity with MCP (Model Context Protocol) standards, tools, and agentic workflows.
Integration with LLM APIs (OpenAI, Anthropic Claude, Llama, Bedrock, Vertex AI) using Spring AI or LangChain4j.
Full Stack Development: Proficiency in frontend web technologies (React.js, Angular, TypeScript, HTML5/CSS3) and backend API integrations.
Cloud & DevOps: Deep expertise in Cloud platforms (AWS/Azure/Google Cloud Platform), Docker, Kubernetes, CI/CD pipelines (Jenkins/GitHub Actions), and infrastructure-as-code.
Location: Based in or willing to work onsite/hybrid in Phoenix, AZ.
