Lead Engineer Architect (Java / Spring Boot / AI Engineering)
First seen by Alion on Oct 15, 2026.
Location: Pune
Experience: 7–11 Years
About the RoleWe are seeking a Lead Engineer / Architect with strong expertise in Java 17, Spring Boot, Python, and AI/LLM engineering to design and build scalable enterprise applications. The role emphasizes AI agents, Model Context Protocol (MCP), vector databases, and graph databases, alongside distributed systems and solution architecture. The candidate will provide hands-on technical leadership and drive architecture decisions for secure, AI-enabled platforms.
Job DescriptionThe role involves designing loosely coupled, API-driven, microservices-based, and event-driven architectures aligned with enterprise standards. You will develop production-grade applications using Java 17, Spring Boot, and Python while integrating AI agents, LLM workflows, and intelligent automation capabilities.
A key focus is building AI-ready platforms that support semantic search, context management, inference workflows, and integration with vector and graph databases. Practical experience with graph-based data modeling and knowledge graph technologies is particularly relevant to the role.
You will collaborate with DevOps, SRE, Data, and Security teams to deliver reliable solutions. The position also requires architectural leadership, performance optimization, observability, secure API design, and effective communication of technical decisions and trade-offs.
Key Responsibilities- Design and implement enterprise-scale architectures using Java 17, Spring Boot, and Python.
- Develop loosely coupled, domain-driven, microservices-based, and event-driven solutions.
- Architect AI-enabled services involving LLMs, AI agents, MCP integrations, and orchestration frameworks.
- Integrate vector databases, semantic search, graph databases, and knowledge graph technologies into AI solutions.
- Design efficient SQL and NoSQL data models and optimize data access using caching technologies.
- Lead hands-on development, code reviews, architecture decisions, and engineering best practices.
- Implement secure APIs, system integrations, monitoring, and performance optimization.
- Collaborate with cross-functional teams to manage delivery priorities, technical risks, and architectural trade-offs.
- Java 17
- Spring Boot
- Python development
- Solution architecture and enterprise application design
- AI/LLM development, AI agents, and related frameworks
- Model Context Protocol (MCP) and AI orchestration patterns
- SQL and NoSQL databases
- Vector databases and semantic search
- Graph databases, GraphDB, and graph-based data modeling
- Microservices, API-first design, and distributed systems
- ORM frameworks and messaging technologies such as Kafka or MQ
- Caching technologies such as Redis and Apache Ignite
- Observability and performance monitoring using Splunk, Dynatrace, or equivalent tools
- API security and secure AI component integration
- Experience with Solr, Lucene, or equivalent search technologies
- Hands-on implementation of GraphDB and knowledge graph solutions
- Exposure to LLM integration, vector databases, and prompt orchestration frameworks
- Experience with large-scale system observability, monitoring, and performance optimization
- Java
- Python
- Spring Boot
The ideal candidate will have strong hands-on software engineering and architecture experience, with expertise in Java, Spring Boot, Python, and AI-integrated systems. Practical implementation experience with graph databases and graph-based data modeling, alongside vector databases, AI agents, and distributed architectures, is especially important. The candidate should be comfortable leading technical decisions while remaining actively involved in development.
Benefits Package- Competitive salary and benefits package
- Opportunities for professional growth and development
- A collaborative and inclusive work environment
- The opportunity to work on exciting and challenging projects with leading clients
Originally posted on Himalayas