Senior TALL Manager

Tredence; Pune District; On-site; INR 2,500,000 - 4,000,000; Full time

Job summary

Tredence in India is seeking an AI Lead - Capability Development to own the end-to-end AI upskilling and workforce transformation strategy. You will bridge Learning & Development with AI business units and delivery teams, translating partner commitments into scalable programs and measurable ROI.

The role requires hands-on curriculum design, program delivery, and reporting, while partnering with leaders to drive adoption and impact across the organization.

Qualifications

• 8–14 years of overall experience in Learning & Development, Talent Transformation, or AI enablement roles.
• Proven experience designing and scaling large cohort-based certification programs (100+ learners).
• Strong understanding of the AI/LLM partner ecosystem—OpenAI, Anthropic, and similar frameworks.
• Demonstrated experience building ROI frameworks for L&D or transformation investments.
• Strong stakeholder management experience with senior leadership and cross-functional teams.
• Experience with instructional design methodologies and modern learning platforms/LMS.
• People management experience with 3+ direct or indirect reports.

Responsibilities

• Define AI capability-building roadmap aligned to enterprise AI strategy and partner commitments.
• Translate leadership commitments into structured, time-bound workforce transformation programs.
• Continuously scan external AI talent and certification landscape to keep the roadmap current.
• Segment the workforce and define differentiated AI fluency and specialization pathways.
• Drive change management and adoption to embed AI capability as a core competency.
• Serve as the primary L&D interface to senior stakeholders across units and external partners.
• Build and maintain governance rhythm with BU heads, practice leads, and sponsors.
• Mentor and upskill internal trainers to ensure high-quality delivery of technical content.
• Own end-to-end program management for all capability-building initiatives.
• Coordinate cross-functional dependencies (IT/LMS, finance, HR) and vendor relationships.
• Manage a team of L&D professionals and cohort delivery staff.

Skills

Executive presence

Analytical mindset

AI fluency

Stakeholder management

Project management

Job description

Job Description

Job Title: AI Lead - Capability Development

Function: Learning & Development (L&D)

Reports To: Head of Learning & Development / Chief Growth Officer (dotted line to AI Business Unit Leadership)

Location: [Insert Location] | Hybrid/Remote as applicable

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

• 8-14 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.
• Ability to balance strategic thinking with hands-on execution - this role is not purely strategic or purely operational.