Manager, Software Development - 149

Infor Inc.; Hyderabad; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Infor Inc. in Hyderabad is seeking a Software Development Manager to lead a small engineering team focused on Java-based SaaS development with AI-assisted tooling. The role requires 6+ years in software engineering, including 4+ years in Java, and leadership experience.

You will guide design reviews, code reviews, and production readiness while contributing code where needed. The position emphasizes AWS-based SaaS delivery, tenant isolation, and secure, scalable multi-tenant applications with

Qualifications

• AI-assisted software development tools for coding, debugging, test generation, documentation.
• Prompt engineering, AI-assisted coding, human-in-the-loop review, validation of AI-generated outputs.
• Java, Spring Boot, REST APIs, SQL, enterprise web applications, service-oriented architecture.

Responsibilities

• Lead delivery execution for a small engineering team.
• Sprint planning, execution, quality checks, release readiness, and production support.
• Provide technical guidance for Java-based SaaS business app development.
• Remain hands-on: contribute code, pair on hard problems, build rapid prototypes with AI tooling.
• Ensure SDLC and Agile practices are followed.
• Mentor team members and share knowledge across development, BA, and QA.

Job description

Manager, Software Development - 149

Department: Development

Employment Type: Full Time

Location: Hyderabad

Description

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.

A Typical Day in the Life Includes:

• 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.

Basic Qualifications:

• 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.

Preferred Qualifications:

• Deeper experience with AI-based software development tools, LLM-based engineering workflows, prompt engineering, agentic development practices, MCP integrations, test generation tools, documentation assistants, or AI-enabled project management workflows.
• Prior experience as a technical lead, senior developer, module lead, or team lead for a small software delivery team.
• Hands-on exposure to AWS cloud services, microservices architecture, DevOps practices, containerization, observability, automated testing frameworks, and performance troubleshooting.
• Experience building or supporting multi-tenant SaaS business applications on AWS or similar cloud platforms, including tenant isolation, scalability, service reliability, configuration management, monitoring, and secure operations.
• Experience coordinating cross-functional delivery across development, BA, QA, product, and support teams.
• Understanding of security, compliance, accessibility, and data privacy considerations in enterprise software development.