TECHNICAL LEAD - Java

Java Tech Lead ? Back-End & Agentic AI
We are seeking an experienced Java Tech Lead to join a cross-functional product team responsible for a customer-facing platform. You will lead the design and delivery of scalable, highly available back-end services while helping the team build secure, production-ready agentic AI capabilities.
This role requires strong technical leadership, hands-on Java expertise, solid architectural judgment, and close collaboration with Product, Front-End, Data/AI, Security, and SRE teams. You will guide engineering standards and technical decisions while remaining actively involved in design, development, and delivery.
What will you do?
Lead the design, development, and evolution of scalable Java back-end services
Build clean, maintainable, testable, and production-ready software
Own technical and architectural decisions across services and integrations
Design distributed systems, microservices, APIs, and event-driven workflows
Develop agentic AI solutions that can reason, plan, use tools, access enterprise data, and execute multi-step workflows
Integrate large language models using frameworks such as Spring AI, LangChain4j, or equivalent technologies
Design retrieval-augmented generation (RAG), tool-calling, memory, and human-in-the-loop patterns
Establish safeguards for AI systems, including access controls, data privacy, prompt-injection protection, output validation, and auditability
Define evaluation approaches for AI quality, reliability, latency, safety, and cost
Provide technical direction, mentoring, and constructive feedback to engineers
Lead code reviews, design reviews, proof-of-concept initiatives, and knowledge-sharing sessions
Collaborate closely with Product and business stakeholders to translate requirements into pragmatic technical solutions
Ensure high standards for performance, availability, observability, security, and operational readiness
Support and improve CI/CD pipelines, automated testing, and engineering delivery processes
Participate in production support, incident analysis, and continuous service improvement
Required knowledge and experience
10+ years of software engineering experience, including experience leading technical initiatives or engineering teams
Strong hands-on experience with modern Java, preferably Java 17 or later
Advanced knowledge of Spring Boot and the broader Spring ecosystem
Experience designing and scaling microservice and event-driven architectures
Strong understanding of object-oriented design, functional programming concepts, design patterns, and clean architecture
Practical experience with Domain-Driven Design, CQRS, and Event Sourcing
Experience building secure, resilient, and high-performance REST and/or GraphQL APIs
Solid understanding of distributed systems, concurrency, fault tolerance, caching, and observability
Hands-on experience with Kafka or similar event-streaming technologies
Cloud-native development experience on AWS
Experience with Docker, Kubernetes, and infrastructure-as-code practices
Experience with relational and non-relational databases
Strong knowledge of automated testing, including unit, integration, contract, and end-to-end testing
Experience with CI/CD pipelines and modern software delivery practices
Experience working in Agile, cross-functional product development teams
Demonstrated ability to lead architectural discussions and communicate technical trade-offs clearly
Agentic AI knowledge and experience
Practical experience building or integrating applications powered by large language models
Understanding of agentic AI patterns, including planning, tool use, workflow orchestration, reflection, memory, and multi-agent collaboration
Experience with Spring AI, LangChain4j, or comparable AI application frameworks
Knowledge of prompt engineering, structured outputs, function calling, embeddings, vector databases, and RAG
Experience integrating hosted or self-managed models from providers such as OpenAI, Anthropic, AWS Bedrock, or open-source ecosystems
Understanding of AI evaluation, hallucination mitigation, grounding, observability, and responsible AI practices
Awareness of the security risks associated with LLM applications, including prompt injection, sensitive-data leakage, excessive agency, and unsafe tool execution
Ability to balance model quality, latency, reliability, privacy, and cost in production systems
Preferred qualifications
Experience/Knowledge with Python for AI services, experimentation, or data-processing workloads
Experience with agent orchestration frameworks or durable workflow platforms
Familiarity with vector databases and hybrid search technologies
Experience with LLM observability, tracing, evaluation, and prompt-management platforms
Knowledge of MLOps, model gateways, and AI governance
Experience modernizing legacy Java platforms or guiding cloud migration initiatives
Experience working in regulated, high-security, or high-availability environments
What will you bring as a colleague?
A hands-on leadership style and a willingness to take ownership
A critical-thinking mindset and the confidence to challenge assumptions constructively
The ability to make pragmatic decisions while maintaining a strong long-term technical vision
A growth mindset and a commitment to developing yourself and others
Strong mentoring, facilitation, and stakeholder-management skills
Curiosity about emerging AI technologies combined with sound engineering judgment
A collaborative approach to solving complex technical and product challenges
Excellent written and verbal communication skills in English

Work arrangement
No