Design, develop, and maintain enterprise-grade applications using Java and Python

Architect and implement graph-based data models and queries to support complex domain relationships

Collaborate with ML and Semantic engineers on designing and training dedicated AI Agents for specific business processes.

Integrate custom Agentic AI solutions (e.g., autonomous agents, multi-agent systems, tool-using agents) into existing enterprise platforms

Collaborate with stakeholders to translate business requirements into robust technical solutions

Optimize application performance, scalability, and reliability in production environments

Participate in technical design discussions, code reviews, and architecture decisions

Contribute to continuous improvement of development workflows and SDLC processes

Candidates must meet all of the following:

Strong, in-depth Java expertise, including:

JVM internals, concurrency, memory management

Spring / Spring Boot or equivalent enterprise frameworks

Professional Python experience, particularly for backend services, scripting, or AI-related integrations

Hands-on experience with Graph Databases, such as:

Neo4j, Amazon Neptune, JanusGraph, TigerGraph, or similar

Graph data modeling and query languages (e.g., Cypher, Gremlin, SPARQL)

Proven experience integrating custom Agentic AI solutions into enterprise applications, including:

Autonomous or semi-autonomous agents

Tool-using or workflow-oriented AI agents

Production integration, not just experimentation or prototypes

Solid understanding of enterprise application architecture, RESTful APIs, and system integration patterns

Experience working with relational databases and modern persistence layers

Strong problem-solving skills and ability to work with complex domains

The following are not required, but will be considered a strong advantage:

Experience with agentic SDLC tools and processes, such as:

AI-assisted development workflows

Agent-driven testing, code generation, or requirement analysis

Familiarity with microservices architectures and distributed systems

Experience with containerization (Docker, Kubernetes) and cloud platforms (AWS)