{"id":1587732,"url":"https://alion.io/job/allianz-lead-consultant-java-j2eespring-boot-3008","title":"Lead Consultant (Java / J2EE|Spring Boot)_3008","company":{"id":1757325,"name":"Allianz","domain":"allianz.com","url":"https://alion.io/company/allianz-com","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":25000,"max_usd":54000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":51},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Angular","optional":false},{"name":"Ansible","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Bamboo","optional":false},{"name":"CI/CD","optional":false},{"name":"Context Engineering","optional":false},{"name":"Dependency Injection","optional":false},{"name":"Embeddings","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Hallucination","optional":false},{"name":"Hibernate","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"JavaScript","optional":false},{"name":"Jenkins","optional":false},{"name":"JUnit","optional":false},{"name":"LLM","optional":false},{"name":"OpenShift","optional":false},{"name":"OWASP Top 10","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"RxJS","optional":false},{"name":"Scrum","optional":false},{"name":"Selenium","optional":false},{"name":"Self-Healing","optional":false},{"name":"Spring Boot","optional":false},{"name":"Spring Security","optional":false},{"name":"SQL","optional":false},{"name":"STRIDE","optional":false},{"name":"TypeScript","optional":false},{"name":"UML","optional":false},{"name":"Java","optional":true}],"status":"closed","first_seen_at":"2026-09-27T00:00:00Z","employer_posted_date":"2026-09-27","last_verified_at":"2026-10-03T17:28:52Z","board_verified":false,"closed_at":"2026-10-03T17:28:52Z","days_open":6,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":6},"description":"A Technical Lead at Allianz is responsible for managing the technical delivery and maintenance of services within a tribe or COE, working across one or more squads. The role balances deep subject matter expertise, technical leadership, and substantial hands-on individual contribution. As an individual contributor, the Technical Lead is expected to actively build, debug, and engineer code as a core, day-to-day part of the role. Technical Leads coordinate the design, development, and testing efforts of develo pers across one or more squads, assist in the mentoring and knowledge sharing of best practices across chapters, and provide support during interviews and talent acquisition. As experts at their craft, AI Technical Leads leverage AI to accelerate the full development lifecycle this can include requirements elicitation to design, build, and test ing. They also providing guidance to product owners, architecture, and delivery managers on risk assessments, estimation, dependency analysis, environment strategies, and solution reviews. They have a strong understanding of the business domain, review and guide analysts in terms of design effectiveness, and are champions of quality and in novation within their tribe and chapters. As hands-on individual contributors, they remain active engineers personally designing, building, debugging, and shipping production code . Manually or via the orstration of AI agents and play a key role in enabling AI-augmented engineering practices across Java-based platforms and services. Technical Leads support the adoption of scalable AI patterns and platforms by collaborating with architecture and platform teams, assessing technical risks, dependencies, and cost implications related to AI usage. They help teams understand AI trade-offs, validate solution designs, and ensure AI components are maintainable, observable, and production-ready. They also promote AI literacy within squads and chapters, mentoring engineers on best practices, quality controls, secure and ethical use of AI, while continuously improving delivery speed, reliability, and overall engineering effectiveness. While AI and agentic coding form a large part of this role, deep hands-on development skills remain critical: the Technical Lead is expected to write, debug, and review code directly and to maintain strong, current engineering fundamentals.\nKEY ACCOUNTABILITIES\nSoftware composition and design : Hands-on design and development of applications and components - actively writing, building, and debugging code - while overseeing the technical design during and after delivery to ensure alignment with business goals.\nAI-assisted Engineering and Solution Enablement: Apply and guide the responsible use of AI-enabled tools and design to improve software development productivity, testing, troubleshooting, and solution quality. Select and standardise approved AI tooling across the squads - evaluating tools for genuine productivity gain, security, compliance, and vendor lock-in - so AI adoption is governed and consistent rather than fragmented. Support teams in integrating AI capabilities into applications where appropriate, while ensuring alignment with architecture standards, security, compliance, and long-term maintainability.\nSoftware Composition, Design & Development Lead hands-on design, development and debugging of applications and components, overseeing technical design throughout and after delivery to ensure alignment with business goals, architecture standards and long term maintainability.\nAI-Assisted Engineering & Solution Enablement Guide the responsible use of AI-enabled tools to improve software development productivity, testing, troubleshooting and solution quality. Select and standardise approved AI tooling across squads - evaluating for genuine productivity gain, security, compl iance and vendor lock-in - ensuring AI adoption is governed, consistent and aligned with architecture standards.\nCode Review, Quality Assurance & Testing Review code, manage merge requests and define quality standards for AI-generated code, maintaining human-in-the-loop validation before production deployment. Leverage AI for autonomous test-case generation and self-healing functional tests to improve cove rage, regression speed and test reliability.\nTechnical Troubleshooting, Performance & Cyber Risk Debug system problems, resolve runtime issues and optimise process performance for reliability and efficiency. Manage cyber risk and vulnerability requirements, keeping applications current and implementing procedures to protect against data misuse, while serving as an advisor to ISO, Cyber and Service Owners on risk matters.\nResponsible, Ethical AI & Cost Management Act as the first line of defence for responsible AI use across squads - guarding against bias, protecting data privacy and ensuring clear human accountability for AI-generated outcomes. Monitor and optimise AI tooling and service costs, including model and token usage, to ensure productivity gains are delivered cost-effectively.\nAI Impact Measurement & Continuous Improvement Define and track metrics for the impact of AI on delivery and quality - including velocity and defect density - confirming that AI adoption produces genuine productivity gains rather than added technical debt, and driving continuous improvement across eng ineering practices.\nCollaboration, Risk Identification & Agile Delivery Collaborate across the agile organisation to provide estimates, refine requirements and guide solution sizing, while proactively identifying risks that may jeopardise project delivery and ensuring transparent disclosure of potential caveats during develop ment.\nTeam Development, Innovation & Talent Foster technical skills growth and business domain knowledge across squads through documentation, brownbags and coaching. Champion innovation and R&D, formulating business cases for new technologies, and support chapter leads in the technical selection of new talent.\nTeam Development : Leads and develops chapter members providing technical guidance and skills training in relevant area of expertiese .\nPerformance monitoring : Able to monitor and evaluate the perofmrance of software developers across one or more suqads, providing feedback to the chapter lead.\nCollaboration and communicaiton fosters collaboration across squads, actively communicates, and ensures proactive information management to guide employees through change initiatives effectively\nAdhere to Diversity and Inclusion policy and principles and help create an environment of respect, collaboration and inclusion for our colleagues and customers.\nEnsure, so far as reasonably practicable, the health and safety of self, colleagues, contractors and\nUnderstand customer insights and feedback. Act to put the best interests of our customers at the heart of everything we do.\nTechnical leads should be a domain expert and be able to guide internal and external stakeholders of solution or delivery options.\nRisk & Compliance\nUnderstand and adhere to all relevant policies and procedures to mitigate risks and compliance issues and take action to identify, report and resolve risks and issues, or escalate as necessary.\nREQUIREMENTS\nUnderstanding of DevSecOps practices, benchmarks and measurements.\nFull-Stack Development Expertise: Proficiency in backend development with Java and J2EE, Spring Boot (REST APIs, microservices, security, JPA/Hibernate), and frontend development using Angular (TypeScript, RxJS, component-based architecture) and JSPs for s erver-side rendering, with hands-on experience in JavaScript, ORM, and rules engines.\nSolution Architecture & Design: Strong skills in designing scalable, maintainable, and secure web applications, including hands-on experience with MVC patterns, dependency injection, and modular architecture.\nIntegration & API Management: Experience integrating with databases (SQL/NoSQL), third-party APIs, and legacy systems, as well as designing and documenting RESTful APIs and managing authentication/authorization (OAuth2, JWT, Spring Security) .\nPrompt Engineering & Instruction Design: Expertise in designing scalable prompt engineering frameworks, including reusable prompt libraries, few-shot patterns, structured reasoning templates, and instruction hierarchies to ensure consistent and reliable AI outputs.\nContext Engineering & Knowledge Assembly: Proven ability to design context assembly systems that dynamically pull from source code, technical documentation, historical tickets, and runtime data to maximize AI effectiveness within token constraints, including retrieval-augmented generation (RAG) over embeddings and vector stores drawing on validated, access-controlled knowledge sources .\nQuality Gates & Compliance Automation: Strong capability in embedding quality controls within autonomous loops, including automated testing, linting, validation, security checks, and compliance enforcement between each AI-driven iteration. Capability in AI evaluation frameworks - measuring groundedness, factual correctness, and regression against curated test sets, including adversarial and red-team testing - and in production monitoring of AI output for drift and hallucination\n.\nEngineering & Operations Automation: Experience scaling AI-powered automation across diverse technology stacks to reduce repetitive engineering effort, modernize codebases and automate vulnerability remediation .\nLeadership, Governance & Financial Control: Knowledge of responsible-AI governance frameworks and auditability, AI cost-management techniques (token tracking, caching, model routing), and AI-native and agentic development practices.\nAI Regulatory Compliance & Standards: Working knowledge of the AI regulatory and governance landscape - including Australia's AI Ethics Principles, the National AI Centre's Voluntary AI Safety Standard and Guidance for AI Adoption, relevant APRA prudential standards (CPS 230 Operational Risk Management and CPS 234 Information Security) and the Privacy Act 1988, together with the international ISO/IEC 42001 (AI management systems) standard - and the abilityto apply risk based controls to high Internal risk AI use cases in financial services, covering data governance, human oversight, transparency, accuracy and robustness, and record keeping.\nDevOps & Quality Assurance: Familiarity with CI/CD pipelines Ansible and toolchains (Jenkins, GitHub Actions , GIT, Bamboo, ), automated testing (JUnit, Selenium, Jasmine/Karma for Angular), code reviews, and performance tuning for both backend and frontend applications. Technical Leadership & Collaboration: Technical leadership, mentoring, and cross including coding standards governance and functional collaboration skills, comprehensive technical documentation. Strong understand of IT controls, development standards and practices and how to rollout and uplift across a number of languages and projects. U ndersanding of secruity concepts across threat modelling (STRIDE), SAST, SCA, and DAST security risks per the OWASP Top 10 for LLM Applications - , extended to AI specific including direct and indirect prompt injection, sensitive information disclosure, insecure handling of model output, data and model poisoning, and excessive agency in agen systems . Significant experience in working on structured (Iterative or Agile Scrum) SDLC process Leadership level design skills in OO Design, UML, domain modelling etc. es. tic Familiariaty with cloud providers (AWS/Azure), containers, spring boot, and container platforms such as Kuberneties or OpenShift.\nUnderstanding of licence management and cost optimisation as it relates to software deployment. Experience in delivering software projects into production environments in Insurance or Financial Services organisations. Strong understanding of enterprise architecture methodologies and frameworks. 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