Sr. Java Developer/Lead
RIVAGO INFOTECH INC.
San Lorenzo, United States of America
yesterday
Role details
Contract type
Permanent contract Employment type
Full-time (> 32 hours) Working hours
Regular working hours Languages
English Experience level
SeniorJob location
San Lorenzo, United States of America
Tech stack
Clean Code Principles
Java
API
Artificial Intelligence
Cloud Engineering
Code Generation
Code Review
Programming Tools
Github
Python
Key Management
OAuth
Object-Oriented Software Development
OpenID
Scrum
JSON Web Token
Secure Coding
Service Development Studio
Software Deployment
Systems Integration
Performance Testing
Real Time Systems
React
Large Language Models
Concurrency
Spring-boot
Backend
FastAPI
Event Driven Architecture
Build Management
Containerization
Angular
Kubernetes
Information Technology
Apache Flink
Kafka
Front End Software Development
Virtual Agents
REST
gRPC
Docker
Jenkins
Microservices
Job description
Senior Developer/ Leads with Java, Python + Google ADK, comfortable working with LLMs, MCP, building Agents, generating code, * Design and build secure, scalable backend services in Java (Spring Boot) and Python, and deliver production-grade AI agent capabilities using Google Agent Development Kit (ADK).
- Build agentic workflows that can use tools, maintain session context/memory, and generate/refactor code with strong human-in-the-loop review and governance.
- Integrate agents with enterprise systems and data sources via Model Context Protocol (MCP) and standard APIs, with strong attention to security, observability, reliability, and cost controls., * Develop high-scale microservices and APIs using Java 17+, Spring Boot, and REST/gRPC; apply solid engineering fundamentals (OOP, concurrency, performance).
- Build Python services (e.g., FastAPI) that host agent logic and AI integrations; implement robust error handling, retries, and structured outputs.
- Create and orchestrate agents using Google ADK: define agents, attach tools/functions, manage sessions and memory, and implement multi-step workflows (sequential/parallel/routed).
- Implement MCP-based integrations: expose internal tools/resources as MCP servers (where applicable) and consume MCP tools from agents/clients securely.
- Deliver LLM features (tool/function calling, prompt & context engineering, evaluation) and implement RAG patterns with embeddings and vector databases when grounding is required.
- Containerize and deploy services using Docker and Kubernetes (AKS/OCP preferred); build CI/CD pipelines (GitHub Actions/Jenkins/Harness) with quality gates.
- Implement observability (logs/metrics/tracing), SLOs/SLIs, and performance testing; participate in incident response and root cause remediation.
- Mentor engineers, perform design/code reviews, and collaborate with product, security, data, and platform teams in an Agile/Scrum environment.
Requirements
- Strong Java backend engineering (Java 17+, Spring Boot, microservices, REST/gRPC).
- Strong Python engineering (OOP, typing, async patterns, packaging) and service development.
- Hands-on experience building agents and workflows with Google ADK (agents, tools, sessions/memory).
- Comfort working with LLMs: tool/function calling, structured outputs, prompt & context engineering, safety considerations.
- Understanding of MCP concepts (resources, tools, prompts; client-server model) and ability to integrate tools using MCP or standard APIs.
- Ability to produce high-quality code with AI-assisted code generation, plus strong review/verification and testing discipline.
- Security fundamentals: OAuth2/OIDC, JWT, secure coding, secrets management; familiarity with mTLS/cert management is a plus.
- Cloud-native fundamentals: Docker, Kubernetes; CI/CD pipelines; basic monitoring/observability.
Good-to-Have Skills
- LangChain/LangGraph or similar orchestration frameworks; experience combining them with ADK where useful.
- Vector DB experience and RAG evaluation practices.
- GCP/Vertex AI (or other cloud LLM hosting) and production deployment patterns (Cloud Run/Agent Engine).
- Kafka/Flink or event-driven architectures for real-time systems.
- Front-end exposure (React/Angular) for agent-driven UIs or developer tooling.