Mandatory skills: AI-Driven SDLC, LLM, AI-Assisted Software Development, SDLC Coaching, Engineering Guardrails, AI SDLC Harness, Threat Modeling, OWASP, AI Code Review, CI/CD Pipeline Integration.
Job Summary
- Serve as the principal technical authority for end-to-end coaching and governance of AI-driven Software Development Lifecycle (SDLC).
- Design robust engineering guardrail frameworks for AI-assisted software development.
- Deliver structured, hands-on coaching across every SDLC phase.
- Standardize repeatable, secure, production-grade AI-augmented engineering workflows.
- Mitigate LLM hallucinations, technical debt, security vulnerabilities, and inconsistent deliverables.
- Improve engineering efficiency using AI without compromising software quality, compliance, or system stability.
Key Responsibilities
1. AI-Driven SDLC Coaching and Hands-On Enablement
- Lead formal training sessions, one-on-one coaching, team workshops, and live code clinics.
- Coach engineers on complete AI-powered SDLC workflows.
- Establish best practices for AI-assisted software development.
2. Security Analysis and Threat Modeling
- Train engineers to use AI tools for automated vulnerability scanning.
- Implement attack surface mapping and threat modeling practices.
- Perform OWASP compliance checks and data leakage risk assessments during the design phase.
3. Code Review Governance
- Establish human-in-the-loop AI code auditing practices.
- Develop checklist-driven code review frameworks.
- Validate AI-generated code for logical correctness, readability, performance, security, and compliance.
4. Unit and Integration Testing
- Train teams to use AI for automated test case generation.
- Guide edge-case identification and enumeration.
- Use AI tools for mock data creation and test coverage validation.
- Develop automated regression test suites.
5. CI/CD Pipeline AI Integration
- Integrate AI tools into CI/CD build and deployment pipelines.
- Implement pre-commit validation gates.
- Enable in-pipeline code scanning and automated test execution.
- Implement artifact auditing and deployment approval automation.
6. AI SDLC Harness Architecture and Tooling
- Design, develop, and maintain enterprise-grade technical harnesses across all SDLC stages.
- Build engineering guardrails to enforce secure and consistent AI-assisted development.
- Implement automated validation gates to block unverified AI outputs early in the lifecycle.
- Enforce mandatory human review checkpoints for high-risk modules.
- Ensure governance and quality controls at every SDLC stage, regardless of AI automation maturity.
Required Qualifications
- 10–15 years of software engineering experience.
- Complete hands-on experience delivering software across the entire SDLC.
- Proven expertise in requirements gathering and analysis.
- Strong experience in security design and threat modeling.
- Hands-on software implementation and development planning experience.
- Experience conducting formal code reviews and automated code audits.
- Strong knowledge of manual and automated testing methodologies.
- Experience in technical documentation and writing.
- End-to-end CI/CD pipeline design and implementation experience for production systems.
- Experience with AI-assisted software development and LLM-based engineering workflows.
- Strong understanding of software security, quality governance, compliance, and engineering best practices.
