- AI-assisted development: AI-assisted software development tools for coding, debugging, test generation, documentation, and productivity improvement.
- AI and LLM practices: Prompt engineering, AI-assisted coding, human-in-the-loop review, validation of AI-generated outputs, responsible AI usage, and practical application of LLMs in SDLC workflows.
- Application development: Java, Spring Boot, REST APIs, SQL, enterprise web applications, service-oriented architecture, and business application development.
- SaaS architecture: Multi-tenant SaaS design, tenant isolation, configurability, scalability, availability, integration patterns, performance, and production readiness.
- Cloud platform: AWS-based application delivery, deployment, monitoring, logging, security controls, CI/CD, and production support.
- Engineering practices: Agile/Scrum, SDLC governance, automated testing, code reviews, secure coding, observability, incident analysis, and continuous improvement.
- DevOps and platform exposure: CI/CD pipelines, containers, Kubernetes or similar orchestration platforms, infrastructure automation, monitoring tools, and cloud operational practices.

- Introduce practical AI-assisted development practices using approved tools and team workflows.
- Help the team use AI tools for requirements clarification, code understanding, first-draft code generation, test case creation, documentation, defect analysis, and productivity improvement.
- Establish basic guardrails for responsible AI usage, including human review, validation of AI-generated output, secure handling of enterprise data, and accountability for final deliverables.
- Lead a small team of developers, business analysts, and QA engineers through sprint planning, execution, quality checks, release readiness, and production support.
- Provide technical guidance for Java-based SaaS business application development, including design reviews, code reviews, troubleshooting,



refactoring, and defect resolution.
- Remain hands-on in the codebase: contribute code, pair on hard problems, and build rapid prototypes and technical spikes with AI-assisted tooling to test feasibility and settle design questions before the team commits to an approach.
- Support development of scalable, secure, multi-tenant SaaS applications on AWS, with attention to tenant isolation, configurability, performance, reliability, observability, and operational readiness.
- Translate business requirements into transparent technical tasks in partnership with BAs, product owners, QA, and engineering stakeholders.
- Ensure the team follows SDLC and Agile practices, including requirements analysis, design, development, testing, deployment, release management, and post-release support.
- Promote engineering discipline through clean code, secure coding, automated testing, CI/CD, source control hygiene, documentation, and peer review.
- Identify delivery risks, technical blockers, quality issues, and dependencies early, and communicate mitigation plans clearly.
- Mentor team members and encourage practical knowledge sharing across development, BA, and QA functions.

The Software Development Manager - Java SaaS (AI-Assisted Development) will lead delivery execution for a small engineering team at Infor s Hyderabad Development Center. The role requires 6+ years of software engineering experience, including 4+ years of hands-on Java development, 2+ years of technical leadership or engineering management experience, exposure to AWS-based SaaS product development, and practical use of AI-assisted software development tools to improve SDLC productivity, quality, and delivery predictability.

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📌 Manager, Software Development - 149 (Hyderabad)
🏢 Infor
📍 Hyderabad