Principal AI Driven SDLC Coach

**Principal AI Driven SDLC Coach

Location: Valencia - CA

Duration: 6 months** Preference to California, but also open to

• St Paul - MN
• Marlborough - MA.

Jd As a Principal AI Driven SDLC Coach, you serve as the senior technical authority responsible for end-to-end coaching and governance of AI-driven full Software Development Lifecycle (SDLC). You design robust

engineering guardrail harnesses and deliver structured hands-on coaching covering every SDLC phase.

You standardize repeatable, secure, production-grade AI-augmented workflows for engineering teams, mitigate LLM hallucinations, technical debt, security vulnerabilities and inconsistent deliverables across the entire development lifecycle, while empowering engineers to maximize AI efficiency without compromising software quality, compliance and stability.

Key Responsibilities

Full AI-Driven SDLC Coaching & Hands-On Enablement Lead formal training, 1:1 deep coaching, team workshops and live code clinics covering the complete AI-powered SDLC workflow:

Spec & Requirements Collection: Coach structured prompt design, user story refinement, ambiguous requirement decomposition, and AI-assisted formal specification drafting; guide teams to avoid vague inputs that cause flawed downstream deliverables.

Security Analysis & Threat Modeling: Train engineers to leverage AI tools for automated vulnerability scanning, attack surface mapping, OWASP compliance checks, data leakage risk assessment at the design phase (shift-left security via AI).

Implementation Planning: Guide AI-assisted architecture drafting, task breakdown, milestone scheduling, dependency mapping and modular development planning to prevent bloated or unmaintainable AI-generated solutions.

AI Code Generation: Establish disciplined vibe coding practices: structured prompt chaining, context injection, incremental code generation, and constrained model output to reduce redundant, buggy or non-idiomatic code.

Code Review Governance: Coach human-in-the-loop AI code auditing; build checklist-driven review frameworks to validate logic correctness, readability, performance and compliance of LLM-generated code.

Unit & Integration Testing: 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.

Automated Documentation Generation: Standardize AI workflows for API docs, design docs, runbooks, comment blocks and release notes; ensure auto-generated documentation stays consistent with actual implemented code.

CI/CD Pipeline AI Integration: Coach embedding AI tools into build pipelines: pre-commit validation gates, in-flight code scanning, test auto-execution, artifact auditing and deployment approval automation within CI/CD workflows.

AI SDLC Harness Architecture & Tooling Build

Design, develop and maintain enterprise-grade technical harnesses that enforce guardrails across every SDLC stage listed above; embed automated validation gates to block unvetted AI outputs early in the lifecycle.

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: requirement clarity pass rate, security flaw escape rate, code rewrite overhead, test coverage ratio, documentation completeness and pipeline failure frequency caused by unregulated AI coding.

Continuously refine harness rules to counter LLM hallucinations, incomplete logic and insecure auto-generated artifacts across all development phases.

Enterprise Standardization & Compliance Governance

Author playbooks, prompt libraries, checklists and workflow templates for each AI SDLC stage for backend, frontend, cloud-native and embedded 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 (authentication, payment processing, PII handling) at every SDLC checkpoint regardless of AI automation maturity.

Required Qualifications Software engineering experience with complete hands-on SDLC delivery

Proven expertise across 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 & LLM workflow expertise

Hands-on experience leveraging code-generating LLMs across all SDLC phases; 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.

Core Competencies

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