Lead Consultant (Java)_3007

Allianz Technology; Maharashtra; Sur place; INR 1 500 000 - 2 000 000; Plein temps

Résumé du poste

Allianz Technology in Maharashtra, India seeks a Technical Lead to own technical delivery and maintenance across one or more squads, blending deep expertise with hands-on coding and architectural guidance. You will mentor engineers, guide AI-enabled development, participate in interviews, and ensure security, quality and performance across Java-based platforms.

The role emphasizes collaboration with product owners and delivery managers, risk assessment, and continuous improvement through

Responsabilités

• Hands-on software design, development and debugging, ensuring alignment with goals.
• Lead AI-assisted engineering and tool standardisation across squads.
• Code review and quality assurance with human-in-the-loop validation.
• Troubleshoot performance, cyber risk, and maintainability across services.
• Collaborate in agile delivery, estimate work and manage risk.

Connaissances

Technical Leadership

Java

AI tooling

Code Review

Problem Solving

Outils

Java

Description du poste

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.

Key Accountabilities

• Software 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.
• AI-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.
• Software 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.
• AI-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.
• Code 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.
• Technical 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.
• Responsible, 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.
• AI 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.
• Collaboration, 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.