Training Lead - Agentic AI

Infosys; Bengaluru; On-site; INR 1,500,000 - 2,000,000; Full time

Job summary

A leading technology consulting firm in Bengaluru seeks an AI Learning Strategist to define AI learning strategies and deliver hands-on learning modules. Responsibilities include designing practical labs, mentoring AI Teams, and driving responsible AI practices aligned to evolving toolchains. Candidates should have expertise in Generative AI and training development. This role offers an opportunity to apply advanced AI tools and methodologies in a dynamic engineering environment.

Responsibilities

• Define the AI learning strategy and roadmap for engineering teams.
• Design and deliver hands-on learning modules on Generative AI and workflows.
• Coach squads during sprints and embed AI tools into developer workflows.
• Curate and maintain high-quality training content aligned to toolchains.
• Drive responsible AI practices including security and evaluation.

Skills

Generative AI

Agile methodologies

Training and development

Data analysis

Mentoring

Job description

Overview

Define the AI learning strategy and roadmap for engineering teams; establish role-based curricula and certification pathways.

Responsibilities

• Design and deliver hands-on learning modules on Generative AI, Agentic workflows, orchestration fabrics, and MCP-based integrations.
• Develop practical labs and simulations that mirror real SDLC stages (discovery, design, build, test, deploy, operate).
• Operationalize training in projects: coach squads during sprints, embed AI tools into developer workflows, and track adoption.
• Curate and maintain high-quality training content (playbooks, videos, labs, assessments) aligned to evolving toolchains and policies.
• Drive responsible AI practices including privacy, security, guardrails, prompt safety, and LLM evaluation.
• Partner with delivery, product, and platform teams to align training with current projects, tech stacks, and compliance requirements.
• Establish metrics and dashboards for impact (usage, skill maturity, productivity, code quality, defect leakage, cycle time).
• Run communities of practice; mentor AI Champions/Coaches and scale enablement through train-the-trainer programs.
• Continuously assess new tools and methods; run pilots and incorporate learnings into updated curricula.