Java Technical Lead

Title: Java Technical Lead - Onsite
Location: Princeton, NJ, USA
Length: Long term
Restriction: W2 or C2C

Description:

Webcam interview; Long term project Onsite

Description:

Job Responsibilities:

Architecture & Development Leadership:

Lead design and development using Java (Java 17) and microservices architecture.
Create scalable, configuration-driven enterprise platform solutions.
Design and review solution implementations ensuring performance, scalability, and compliance.
Collaborate with internal and external stakeholders to define scope boundaries, transaction management, and data source integration.

Cloud & Containerization:

Drive technical strategies and hands-on development using Azure Cloud, Kubernetes, Docker, and OpenShift platforms.
Support integration of managed environments into broader platform ecosystems (e.g., managed analytics environments).
Stay current with tools, frameworks, and best practices across cloud-native and Web3.0 technologies.

Semantic Modeling & Data Frameworks:

Implement semantic modeling frameworks and solutions using RDF, OWL, SHACL, GraphDB, and SPARQL.
Conduct proof-of-concepts (POCs) and define best practices for semantic architecture.

Leadership & Mentorship:

Mentor and coach development teams on architecture and implementation best practices.
Take ownership of delivery and ensure timely, high-quality solutions from development teams.

Core Skills & Qualifications:

15+ years of software engineering experience with deep Java and Microservices expertise.
Strong hands-on experience with Java 17, Azure Cloud, Kubernetes, Docker, and OpenShift.
Proven expertise in semantic modeling, including experience with RDF, OWL, SHACL, SPARQL, and GraphDB.
Experience in Web 3.0 technologies and enterprise platform configurations.
Ability to define architecture, conduct reviews, and lead cross-functional delivery teams.
Strong communication skills and stakeholder collaboration.

Nice-to-Have:

Experience integrating managed analytics environments.
Prior experience in large-scale enterprise platform implementations.
Exposure to AI or ML integration with semantic models.