Practice Lead — AI & Data (APAC)

Role Overview
Act as the hands-on AI technical leader for FPT's consulting practice. You design AI strategies, architect end-to-end solutions, and lead client workshops translating business needs into scalable AI systems for regulated industries. This is a producing role: you draw the architecture, write the playbook, and run the workshop yourself.
You will work closely with leadership, other CoE Directors, and FPT's offshore AI engineering teams. If you want to step out of a large firm's hierarchy, build something from scratch, and accelerate your path to Practice Lead or MD
Responsibilities
1. AI Practice & Solution Architecture (40%)

Lead AI strategy, architecture, and solution design across GenAI, ML, data platforms, and MLOps/AIOps

Build reusable consulting IP: AI readiness frameworks, reference architectures, operating model designs, and proposal templates

Produce key technical artefacts yourself with architecture blueprints, data pipeline designs, solution specs not just review or delegate

Write PRDs for FPT's FleziPT AI platform based on real client demand; review offshore engineering outputs

Design and deliver an AI skills curriculum to upskill FPT's Solution Architects and Business Analysts (AI strategy framing, use-case economics, data readiness, Agentic AI, Digital Workers)

2. Client Engagement & Pre-sales (35%)

Run AI workshops directly with CTOs, CDOs, Enterprise Architects, and data/ML engineering leads

Lead end-to-end proposal development and solutioning for AI transformation engagements

Actively contribute to pipeline growth by identifying expand opportunities within existing accounts and shaping new pursuits with the MD

3. Delivery Oversight (15%)

Brief and direct offshore AI/engineering teams with structured requirements, milestone reviews, and quality gates

Act as technical quality gate review solution outputs before client presentation

Work with the Legacy Modernisation Director to ensure modernisation designs are AI-ready: observability, telemetry, data layer, automation hooks

Be ready to go hands-on with AI engineering (even coding level) when a critical situation demands it

4. Thought Leadership (10%)

Support C-suite engagements alongside the MD

Co-develop AI thought leadership: industry POVs, solution briefs, and conference presentations

Requirements

12+ years in AI/ML, data engineering, or technical consulting, with genuine hands-on development or model-building experience

End-to-end experience delivering AI/data transformation programmes and whiteboard to production, not just POCs or advisory

Strong domain exposure in healthcare or banking (regulated environments in APAC)

Deep expertise across two or more of: GenAI/LLMs, ML platforms, MLOps/AIOps, data pipelines, cloud-native AI services

Demonstrated ability to produce architecture designs and lead client workshops at CTO/CDO level independently

T-shaped profile: frame AI investments in business outcomes, not model metrics — run a C-suite AI strategy session AND a hands-on architecture deep-dive

Both advisory and implementation experience: you have shaped AI strategy AND overseen delivery

Hands-on experience creating consulting IP and you have personally written AI frameworks, assessment tools, or reference architectures used by others

Experience directing offshore teams across time zones to briefing engineers, reviewing outputs, giving structured feedback

Strong executive communication and comfort in client-facing pre-sales situations

AI use cases in banking (fraud, risk, automation) or healthcare (clinical NLP, decision support, care pathway optimisation, Agentic AI)

Knowledge of AI governance and APAC regulations (MAS FEAT, TRM guidelines, health data privacy)

Exposure to AI-assisted modernisation tooling (code analysis, automated refactoring, AI-augmented testing)

Experience co-selling with hyperscalers (AWS, Azure, GCP) in an AI context

Regional APAC experience across multiple markets

MBA or postgraduate qualification

Originally posted on Himalayas