P1 - GP - AI-First Software Development Lead

Job Description – AI-First Software Development Lead

About Us:

Atlas Systems Inc. is a software solutions company headquartered in East Brunswick, NJ. Incorporated in 2003, Atlas provides solutions across GRC, Technology, Procurement, Healthcare Provider and Oracle to customers globally. Atlas helps organizations modernize applications, data platforms and business operations.

For more information, please visit our website: https://www.atlassystems.com/

Please click on the link below to apply for this position: https://atlas.bamboohr.com/careers/632

Job Title: AI-First Software Development Lead (Business Analyst + Scrum Master + Hands-on Technical Lead)

Location: Bangalore, India (Work from Office / Hybrid as applicable)

Work Timing: IST hours; flexibility to collaborate with customer teams as required

Experience: Typically 8+ years in software development and delivery, including hands-on coding, business analysis and Agile team leadership.

Engagement Type: Long-term Contract or Full-Time

Position Summary

We are seeking a hands-on AI-First Software Development Lead who combines business analysis, Scrum Master / Agile delivery leadership and technical leadership in one role. The successful candidate will translate business problems into clear, testable requirements; guide a team through predictable delivery; and personally design, build, review, test and troubleshoot production-quality software using approved AI-assisted engineering tools.

This is not a purely coordination or architecture role. The lead remains technically hands-on and accountable for business value, delivery execution, engineering quality, team flow and production outcomes across the software development lifecycle.

Key Responsibilities

Business Analysis & Product Discovery

• Engage customers and users to understand business goals, pain points, workflows, constraints and measurable success criteria.
• Facilitate discovery and process mapping; document business rules, current/future-state workflows and functional / non-functional requirements.
• Convert needs into epics, user stories, acceptance criteria and process flows; prioritize the backlog and own UAT readiness.

Scrum Mastery & Agile Delivery

• Facilitate sprint planning, daily stand-ups, reviews, retrospectives, backlog refinement and release planning focused on outcomes.
• Maintain clear sprint goals, Definition of Ready / Done, manageable work in progress and transparent delivery forecasts.
• Remove blockers and manage dependencies, risks, decisions and escalations across business, engineering, QA, security and operations.
• Coach teams in Scrum / Kanban, estimation, collaboration and continuous improvement; communicate status and trade-offs clearly.

Hands-on Technical Leadership & Engineering

• Remain hands-on: write, debug and refactor production code, build APIs, integrations and UI components, and resolve critical technical issues.
• Translate requirements into pragmatic solution designs, modular architecture, interface contracts, data models and actionable technical tasks.
• Conduct code reviews and pair-programming; mentor developers and set standards for maintainability, testability and secure-by-design implementation.
• Promote automated testing, Git, code quality gates, CI/CD, deployment automation and production readiness.

AI-First Delivery, Quality & Responsible Use

• Embed approved AI tools into discovery, requirements, design, coding, testing, documentation, migration and support workflows.
• Use AI assistants for code, tests and documentation with human verification; check correctness, security, privacy, licensing and data exposure.
• Measure impact through cycle time, escaped defects, test effectiveness, rework and customer value—not AI usage volume alone.
• Maintain traceability from requirements to code, tests and release evidence, and follow Atlas / customer policies for data, access and intellectual property.

Stakeholder, Team & Production Ownership

• Act as an accountable point across business, delivery and engineering with clear decisions, ownership and escalation paths.
• Balance scope, quality, timeline, cost, technical debt and operational risk; support release notes, runbooks and post-release reviews.
• Build a culture of customer focus, flexibility, agility, precision, accountability and continuous learning.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Technology or a related discipline, or equivalent practical experience.
• Typically 8+ years of software application development and delivery experience, including hands-on coding and cross-functional team leadership.
• Experience in business analysis: stakeholder interviews, requirements elicitation, user stories, acceptance criteria, backlog management and UAT.
• Practical Scrum Master / Agile facilitation experience, including sprint ceremonies, impediment removal, team coaching and delivery improvement.
• Solid software engineering fundamentals in at least one modern programming language and application stack, with ability to review, debug and deliver production code.
• Experience delivering APIs, web applications, integrations, databases and automated tests; familiarity with Git, code reviews and CI/CD.
• Practical use of AI-assisted development tools in real engineering workflows, with disciplined verification and secure use.
• Strong communication, structured problem-solving and stakeholder-management skills; able to move effectively between business, delivery and technical detail.

Technical Skills

Business Analysis & Agile Delivery

• Requirements discovery, process mapping, user stories, acceptance criteria, backlog prioritization, UAT, Scrum, Kanban, sprint planning, flow metrics and retrospectives.

Programming & Application Engineering

• One or more of Java, C#, Python, JavaScript / TypeScript or equivalent; web frameworks, REST APIs, integrations, SQL / data modelling and debugging.

AI-First Development

• Approved coding copilots / LLM assistants, context and prompt design, AI-generated tests and documentation, evaluation and human-in-the-loop review.

Quality & DevOps

• Unit/integration/API testing, Git, CI/CD, code quality tools, observability, logging, deployment automation and root-cause analysis.

Security & Responsible AI

• Secure coding, least privilege, secrets handling, privacy, dependency scanning and responsible handling of customer data and intellectual property.

Preferred / Nice-to-Have Skills

• Experience with AI agents or Model Context Protocol (MCP), cloud-native platforms, containers, workflow automation, retrieval-augmented generation (RAG) or enterprise data platforms.
• Experience delivering software in regulated customer environments and using AI to improve engineering productivity while maintaining quality and security.

Core Competencies

• Business-first thinking and ability to connect requirements to measurable customer outcomes.
• Hands-on technical credibility and willingness to contribute production code—not just assign or review work.
• Facilitative leadership, collaboration, coaching, conflict resolution and decisive prioritization.
• Pragmatism, ownership, adaptability and comfort operating in a changing client environment.
• AI fluency balanced with critical thinking, confidentiality, quality discipline and human accountability.
• Agile execution, risk management, stakeholder communication and continuous improvement.
• Engineering quality, secure-by-design thinking and disciplined production ownership.
• Clear written and verbal communication with business and technical audiences.
• Problem solving, learning agility and effective cross-functional collaboration.
• Customer focus, precision, accountability and commitment to outcomes.

Success Measures

• Requirements are clear, prioritized, testable and traceable to business outcomes, reducing ambiguity and avoidable rework.
• Sprints and releases demonstrate improved predictability, healthy flow, timely risk escalation and stakeholder alignment.
• The lead contributes meaningful code and technical problem-solving while raising the quality and capability of the wider team.
• AI-assisted practices measurably improve cycle time, test effectiveness, documentation or rework without increasing security risk or escaped defects.
• Production releases meet agreed quality, security, performance and operational-readiness expectations; incidents result in durable corrective actions.
• Customers recognize responsive communication, ownership, precision and consistent delivery of business value.