Dear Applicants,
Gen AI Trainer (Technical Focus)
Gen AI trainer for training technical participants:
Location Mumbai ; Budget : 18-22 LPA.
Role Summary
The Technical Generative AI Trainer will be the subject matter expert responsible for enabling and accelerating software development teams by integrating and training them on AI-powered code generation and developer tools (e.g., GitHub Copilot, Cursor-AI, CodeWhisperer). This role focuses on maximizing code quality, security, and developer velocity within the Software Development Life Cycle (SDLC) through expert usage of Gen AI.
Key Responsibilities
1. Technical Training & Curriculum Development
Tool Mastery Training: Design and deliver hands-on, live-coding workshops focused on the advanced features of tools like GitHub Copilot Chat, Copilot Agents, and Copilot CLI to experienced developers.
Prompt Engineering for Code: Train developers on advanced prompt engineering techniques specific to code generation, including context management, persona creation, and chaining prompts to handle complex, multi-file tasks.
Domain-Specific Enablement: Tailor training sessions to specific technology stacks (e.g., Java/Spring, Python/Django, JavaScript/React, Golang) used by different teams, ensuring relevance to their daily projects.
2. SDLC Integration & Optimization
Code Quality & Testing: Instruct on using Gen AI tools to rapidly generate unit tests, integration tests, and performance benchmarks (e.g., using Copilot to generate PyTest/JUnit cases), and analyze/refactor existing code for efficiency.
Security & Compliance: Train on leveraging Gen AI for security vulnerability scanning and prompt engineering to write secure code from the start, adhering to organizational and industry security standards.
Documentation & Knowledge Transfer: Teach best practices for generating clear, accurate technical documentation, API specifications, and READMEs using Gen AI tools to reduce technical debt.
3. Governance & Best Practices
Configuration & Policy: Advise development leadership and DevOps teams on configuring and governing AI code tools at the organizational level (e.g., managing data retention, content exclusion policies, and setting up enterprise licensing).
Measure Impact: Define metrics (e.g., DORA metrics, time-to-first-commit, test coverage) and establish feedback loops to measure the quantitative productivity gains achieved by developers post-training.
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