Manager, Software Development - 149

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.

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 clear 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.

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.