Senior TALL Manager

Tredence Inc.; Maharashtra; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Tredence Inc. in India is seeking an AI Lead – Capability Development to own the end-to-end strategy, design, and execution of AI upskilling and workforce transformation.

The role collaborates with Learning & Development, AI business alliances, and delivery teams to translate partner commitments into scalable capability-building programs. You will design curricula, manage a portfolio of certification initiatives, and mentor internal trainers while demonstrating ROI on L&D investments across

Qualifications

• Experience in Learning & Development or AI enablement roles.
• Designed and scaled large cohort-based certification or upskilling programs.
• ROI and business-case development experience for L&D/Transformation.
• Strong stakeholder management with senior leadership (VP/CxO).
• People management experience with 3+ direct/indirect reports.

Responsibilities

• Define and own AI capability-building roadmap aligned to enterprise strategy and partner commitments.
• Translate commitments into structured, time-bound workforce transformation programs.
• Build and maintain governance rhythms with BU heads and executive sponsors.
• Act as SME on AI skill taxonomies, partner requirements, and curriculum accuracy.
• Design cohort structures, learning journeys, and LMS integration for scalable delivery.
• Own end-to-end program management across concurrent initiatives and vendor relationships.
• Lead and develop a team of L&D professionals and instructional designers.

Skills

L&D / Talent Transformation

Stakeholder management

Instructional design

Project management

Team leadership

Job description

Job Description

Job Title: AI Lead – Capability Development

Job Summary

The AI Lead – Capability Development owns the end-to-end strategy, design, and execution of the organization’s AI upskilling and workforce transformation agenda. This role sits at the intersection of Learning & Development, AI Business/Alliance teams, and Delivery organizations – translating partner ecosystem commitments (e.g., OpenAI, Anthropic, and other AI/LLM providers) into a scalable, measurable capability-building engine. The role is accountable for building the talent pipeline via certification pipeline (e.g., partner-led programs and internal capability tracks), lateral & campus upskilling/cross-skilling- driving cross-functional stakeholder alignment, designing curriculum with business relevance, demonstrating ROI on L&D investment, and managing a team and portfolio of concurrent capability-building initiatives. This is a strategic-cum-execution role – the person will need to operate as a thought partner to leadership while remaining hands‑on with curriculum design, program delivery, and reporting.

Key Responsibilities

1. AI Strategy & Workforce Transformation

• Define and own the organization’s AI capability‑building roadmap, aligned to enterprise AI strategy, partner commitments, and evolving market/client demand for AI-skilled talent.
• Translate leadership commitments (e.g., "100 certifications," partner-tier requirements, FDE pod readiness) into structured, time‑bound workforce transformation programs.
• Continuously scan the external AI talent and certification landscape to keep the internal roadmap current and competitive.
• Segment the workforce by role archetype and define differentiated AI fluency and specialization pathways for each.
• Drive change management and adoption strategy to embed AI capability as a core organizational competency, not a one‑time training event.

2. Stakeholder Management

• Serve as the primary L&D interface to senior stakeholders across Business Units, Growth/Partnerships, Delivery Leadership, HRBPs, and external partner teams.
• Manage expectations and commitments made by leadership (e.g., certification numbers, timelines) by translating them into deliverable execution plans, and proactively flag risks/blockers.
• Build and maintain governance rhythm (steering committees, monthly/quarterly reviews) with Business Unit heads, Practice Leads, and Executive Sponsors to report progress, risks, and course corrections.
• Act as a trusted advisor to leadership on capability gaps, talent readiness, and workforce risk related to AI adoption.
• Manage external partner relationships ( license utilization tracking, escalations on training platform issues).

3. Business Collaboration & Curriculum Design

• Partner with Business Unit and Practice Leaders to identify role‑specific capability needs.
• Co‑design curriculum architecture spanning foundational AI literacy, tool‑specific certification, and applied/project‑based learning.
• Blend external certification pathways (partner‑provided) with internally built modules addressing organization‑specific tools, use cases, and client delivery contexts.
• Ensure curriculum is continuously validated against real project/delivery needs via SME input and delivery leadership feedback loops - not designed in a vacuum.
• Own the learning experience design: cohort structuring, learning journeys, blended formats (self‑paced, instructor‑led, hands‑on labs, capstone projects), and platform/LMS integration.

4. ROI & Business Case Development

• Define and track measurable outcomes for all capability‑building investments - completion rates, certification pass rates, badge issuance, deployment readiness, and downstream business impact (e.g., billable utilization of certified talent, win‑rate impact on AI‑related deals, client satisfaction on AI delivered engagements).
• Build the business case and cost‑benefit model for capability investments, including license costs, platform fees, SME/trainer time, and opportunity cost of learning hours.
• Establish a measurement framework (Kirkpatrick or equivalent) to assess learning effectiveness at reaction, learning, behavior, and business‑impact levels.
• Present ROI dashboards and impact narratives to leadership, linking capability development spend directly to revenue enablement, delivery quality, and partner‑tier progression (e.g., OpenAI Select → Advanced → Elite tier requirements).
• Recommend course corrections, reallocation, or scale‑up of programs based on data‑driven insight.

5. Subject Matter Expertise (SME)

• Maintain deep, current knowledge of the AI/LLM ecosystem - foundation model providers, partner certification frameworks, agentic AI, FDE/Forward Deployed practices, prompt engineering, and enterprise AI deployment patterns.
• Act as an internal SME and point of escalation for curriculum content accuracy, partner program requirements, and emerging AI skill taxonomies.
• Represent the organization in partner enablement calls, curriculum advisory sessions, and industry forums to stay ahead of program changes (e.g., shifts in OpenAI's certification structure, new Anthropic Academy offerings).
• Mentor and upskill internal trainers/facilitators to ensure consistent, high‑quality delivery of technical content.

6. Project Management

• Own end‑to‑end program management for all capability‑building initiatives - planning, resourcing, timelines, risk management, and stakeholder communication.
• Manage concurrent workstreams (e.g., multiple partner cohorts running in parallel, each with different license validity windows, e.g., 30‑day expiry constraints) without slippage.
• Set up and maintain governance artifacts: project plans, RAID logs, status dashboards, and milestone tracking visible to leadership.
• Coordinate cross‑functional dependencies (IT/LMS teams, partner admins, finance for licensing spend, HR for workforce data) to ensure smooth program execution.
• Manage vendor/partner relationships operationally - license provisioning, admin access, cohort scheduling, and issue escalation.

7. Team Management

• Build, lead, and develop a team of L&D professionals, instructional designers, and/or program coordinators supporting the AI capability agenda.
• Set clear goals, growth plans, and performance expectations for the team; conduct regular 1:1s, feedback, and career development conversations.
• Foster a culture of curiosity, continuous learning, and ownership within the team - modeling the same AI fluency the team is enabling across the organization.
• Manage capacity planning and workload distribution across concurrent programs and cohorts.
• Identify and develop future L&D/AI capability leaders through delegation and stretch assignments.

Required Qualifications

• 8 –15 years of overall experience, with at least 3-5 years in Learning & Development, Talent Transformation, or AI/Technology Enablement roles.
• Proven experience designing and scaling large cohort‑based certification or upskilling programs (100+ learners), ideally involving external technology partners (cloud, AI/LLM providers, or SaaS platforms).
• Strong understanding of the AI/LLM partner ecosystem - familiarity with OpenAI, Anthropic, data, cloud, or equivalent partner network structures, certification frameworks, and enablement models is highly preferred.
• Demonstrated experience building ROI/business case frameworks for L&D or transformation investments.
• Strong stakeholder management experience with senior leadership (VP/CxO level) and cross functional teams.
• Experience with instructional design methodologies and modern learning platforms/LMS.
• People management experience - has led teams of 3+ direct or indirect reports.

Preferred Skills & Attributes

• Executive presence with the ability to influence without direct authority.
• Analytical mindset - comfortable building and defending data‑driven ROI narratives.
• High personal curiosity about AI - actively uses AI tools and stays current with the fast‑evolving landscape.
• Structured project management discipline combined with comfort operating in ambiguity (partner programs, certification structures, and timelines shift frequently in this space).
• Strong written and verbal communication skills for both technical and non‑technical audiences.