Role Overview:As a Principal AI Driven SDLC Coach, you will serve as the senior technical authority responsible for end-to-end coaching and governance of the AI-driven full Software Development Lifecycle (SDLC). This role involves designing robust engineering guardrail harnesses and delivering structured hands-on coaching across every SDLC phase. The coach will standardize repeatable, secure, production-grade AI-augmented workflows for engineering teams, focusing on mitigating LLM hallucinations, technical debt, security vulnerabilities, and inconsistent deliverables, while empowering engineers to maximize AI efficiency without compromising software quality, compliance, and stability.
Key Responsibilities:- Lead formal training, 1:1 deep coaching, team workshops, and live code clinics covering the complete AI-powered SDLC workflow.
- Coach structured prompt design, user story refinement, ambiguous requirement decomposition, and AI-assisted formal specification drafting.
- Train engineers to leverage AI tools for automated vulnerability scanning, attack surface mapping, OWASP compliance checks, and data leakage risk assessment at the design phase.
- Guide AI-assisted architecture drafting, task breakdown, milestone scheduling, dependency mapping, and modular development planning.
- Establish disciplined vibe coding practices: structured prompt chaining, context injection, incremental code generation, and constrained model output.
- Coach human-in-the-loop AI code auditing and build checklist-driven review frameworks for LLM-generated code.
- Train teams to use AI for test case auto-generation, edge case enumeration, mock data creation, automated test coverage validation, and regression test suite construction.
- Standardize AI workflows for API docs, design docs, runbooks, comment blocks, and release notes, ensuring consistency with implemented code.
- Coach embedding AI tools into CI/CD pipelines for pre-commit validation gates, in-flight code scanning, test auto-execution, artifact auditing, and deployment approval automation.
- Design, develop, and maintain enterprise-grade technical harnesses that enforce guardrails across every SDLC stage, embedding automated validation gates.
- Integrate code LLMs, static/dynamic analysis tools, security scanners, test runners, and doc generators into unified pipeline tooling natively hooked into existing CI/CD platforms.
- Build observability dashboards to measure SDLC efficiency metrics and continuously refine harness rules to counter LLM hallucinations and insecure auto-generated artifacts.
- Author playbooks, prompt libraries, checklists, and workflow templates for each AI SDLC stage for various engineering teams.
- Collaborate with cybersecurity, legal, DevSecOps, and compliance teams to bake license auditing, IP validation, sensitive data filtering, and regulatory requirements into AI SDLC harness gates.
- Enforce mandatory human review gates for high-risk modules at every SDLC checkpoint.
Required Skills:- Software engineering experience with complete hands-on SDLC delivery, including requirements gathering, security design, implementation planning, formal code review, manual/automated testing, technical writing, and end-to-end CI/CD pipeline design for production systems.
- Deep practical AI coding and LLM workflow expertise, including advanced prompt engineering, output constraint design, and mitigation of AI hallucinations and logical defects.
- Experience building custom wrapper tooling/harnesses to govern and validate AI outputs in pipelines.
Qualifications:- Lifecycle-first mindset, prioritizing full SDLC robustness over isolated fast code generation.
- Structured coaching style adaptable for junior to staff-level engineers.
- Strategic risk balancing: accelerating delivery via AI while locking in security, maintainability, and compliance.
- Strong cross-team communication and technical documentation capabilities.
