Staff Engineer - GenAI Platforms & Agentic SDLC

General Summary

Role Overview We are seeking a highly experienced Staff Engineer to drive the design, development, and scaling of GenAI-powered platforms, agentic workflows, and AI-native SDLC capabilities across CPSE. This role will be central to transforming GenAI from pilot initiatives into production-grade, enterprise-scale capabilities, enabling engineering teams to accelerate development, improve quality, and operationalize AI-driven workflows.

Minimum Qualifications

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
• OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
• OR PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
• 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

Key Responsibilities

• Platform, Skills Integration Development - Design and build GenAI platform capabilities, including reusable AI skills and agents MCP servers and orchestration frameworks Tooling and integrations for internal platforms and enterprise data sources Ensure seamless integration with development, testing, and operational systems.
• Reusable Assets Enterprise Adoption - Develop and curate reusable prompts, skills, and workflows to accelerate adoption across engineering teams. Establish validation frameworks to ensure quality, accuracy, and robustness of AI-driven assets. Drive standardization and reuse across teams and business units
• Enterprise AI Workflow Operationalization Agentification - Lead the operationalization of QGenie-based workflows across CPSE use cases. Transform and agentify existing engineering workflows into scalable, reusable AI-driven solutions. Identify and prioritize high-impact workflows for automation and AI enablement across teams
• Agentic SDLC Transformation - Define and implement agent-driven SDLC workflows spanning: Coding and code generation Code review and validation Test automation and coverage Release and deployment orchestration Debugging and root-cause analysis Production monitoring and operations Enable end-to-end automation and intelligent orchestration across the SDLC lifecycle.
• Governance, Observability Traceability - Establish frameworks for: AI governance and compliance Observability of GenAI systems Traceability of decisions and outputs Ensure enterprise readiness with security, auditability, and reliability in production environments.
• Cross-Org Collaboration Scale-Out - Partner with QGenie teams, Business Units (BUs), and GEO organizations to: Drive adoption across regions Align on platform capabilities and priorities Enable scaling from pilots to enterprise-wide deployment Act as a technical leader and evangelist for AI-native engineering transformation.

Required

• Bachelors or Masters degree in Computer Science, Engineering, or a related field.
• 812 years of experience in software engineering or platform development.
• Strong hands-on experience in:
• Distributed systems, cloud platforms, and APIs
• AI/ML systems, LLMs, or GenAI frameworks
• Developer tools, CI/CD, and SDLC processes
• Proven track record of building scalable platforms or frameworks used across teams.

Preferred

• Experience in:
• Agentic systems, workflow orchestration, or AI copilots
• Prompt engineering, evaluation frameworks, and LLM integrations
• Observability platforms and production AI systems
• Exposure to enterprise-scale AI adoption programs or platform engineering

Leadership Expectations

• Demonstrates technical depth and architectural leadership.
• Drives cross-functional initiatives with measurable business impact.
• Mentors engineers and shapes organization-wide engineering practices.
• Brings a data-driven approach to evaluating AI effectiveness and adoption.

Impact Scope

• Direct impact on scaling GenAI adoption across thousands of engineers.
• Enables standardized, reusable, and production-ready AI capabilities.
• Plays a critical role in transforming CPSE into an AI-first engineering organization.