AI Engineering Lead

Role Overview

TSC builds AI-native stakeholder and issue intelligence products for enterprise teams operating across public affairs, regulatory, reputational, strategic and external-risk environments. We are hiring an AI Engineering Lead to own the technical strategy and delivery of the intelligence layer behind Genie.

This is a hands-on product and engineering leadership role, not an AI research-lab position. You will lead AI Engineers and Data Engineers, partner closely with Product and Platform Engineering, and turn fragmented, fast-changing public and proprietary data into reliable, explainable, secure and commercially valuable intelligence.

Your mandate covers production AI systems, data foundations, evaluation and governance, unit economics, and team leadership. Success will be measured not by model novelty, but by customer value, reliability, delivery velocity and sustainable cost.

Location: Hybrid (Ho Chi Minh City)

The Mandate

Own the technical strategy, systems, team and operating model required to turn TSC's data into reliable, differentiated and commercially valuable AI-powered intelligence.

What You Will Own

1. AI Products and Intelligence Systems

• Define TSC's AI technical strategy across stakeholder mapping, classification, summarisation, entity resolution, risk detection, insight generation and workflow automation.
• Identify and frame high-value AI opportunities based on customer problems, decision workflows and TSC's differentiated data assets.
• Partner with Product to translate ambiguous customer needs into measurable product requirements, acceptance criteria and release sequencing.
• Define the appropriate technical approach for each use case, including deterministic software, machine learning, retrieval, LLMs, agents and human review.
• Own the end-to-end lifecycle of material AI capabilities, from data and evaluation design through deployment, monitoring, incident response and continuous improvement.

2. Evaluation, Reliability and Governance

• Build TSC's evaluation system for AI products, including golden datasets, regression tests, retrieval-quality checks, extraction and hallucination metrics, human-review thresholds and business KPIs.
• Define release-quality standards and ensure AI features are observable, testable, traceable and reversible.
• Establish governance for model and agent use, including prompt injection, data leakage, tool permissions, approval workflows, audit logs and customer-specific restrictions.
• Define policies for confidential customer data, workspace isolation, access controls, retention, deletion, data residency and secure use of external model providers.
• Create clear incident, rollback and escalation processes for AI-related quality, security and operational failures.

3. Data Foundations

• Own the data foundations behind TSC's intelligence products: ingestion, transformation, enrichment, entity resolution, labelling, retrieval, freshness, telemetry and feedback loops.
• Improve the quality and usability of fragmented public, proprietary and customer-specific data used by Genie.
• Ensure data pipelines and retrieval systems are reliable, secure, maintainable and fit for enterprise-facing use.

4. Architecture and AI Unit Economics

• Architect production AI systems using LLM APIs, specialised and open-source models, retrieval, deterministic orchestration and tool-using agents where appropriate.
• Make explicit decisions on when to use agents, when deterministic workflows are safer, and when human review is required.
• Own AI unit economics across model routing, caching, context-window discipline, token usage, latency, fallbacks and vendor trade-offs.
• Balance customer value, quality, speed, cost, security and maintainability in architecture and roadmap decisions.
• Maintain clear technical decision records and communicate trade-offs to executives and non-technical stakeholders.

5. Agentic Systems and AI-Enabled Engineering

• Establish repeatable agentic workflows in which AI systems can plan, act, validate, diagnose and revise within defined permissions, budgets and stopping conditions.
• Apply these patterns to customer-facing intelligence products and, where valuable, to internal software and data engineering workflows.
• Design agent workflows around evaluations, tests, logs, version-controlled artefacts, issue trackers, search and browser tools, sub-agents and human approval gates.
• Set permission boundaries, escalation paths, stopping criteria and cost limits so AI tools improve velocity without creating uncontrolled autonomy.
• Build a culture of evidence-driven AI engineering rather than unchecked prompt experimentation.

6. Team Leadership and Cross-Functional Delivery

• Hire, lead, mentor and grow a high-agency team of AI Engineers and Data Engineers.
• Create strong operating practices across technical planning, documentation, code review, evaluation, quality gates, incident learning and architectural decision-making.
• Partner closely with Product, Platform Engineering, Customer Success and company leadership without allowing AI to become an isolated function.
• Support strategic customer and prospect conversations by explaining TSC's AI architecture, quality controls, security model, limitations and product direction in clear commercial terms.
• Build a team structure and capability roadmap that matches TSC's product priorities and stage of growth.

How You Will Work

You will own TSC's AI technical strategy and the delivery capability behind it. Product will own customer problems, product outcomes and roadmap prioritisation. Platform Engineering will own shared application infrastructure and production operations. You will work jointly on architecture, sequencing, release readiness and measurable customer outcomes.

What Success Looks Like

Within the first 6 months, you will have:

• Established a clear AI and data architecture aligned to Genie's highest-value customer workflows.
• Introduced evaluation, telemetry and release-quality thresholds for all material AI features.
• Improved the reliability, traceability, latency and unit economics of production AI systems.
• Defined clear standards for agent permissions, customer data isolation, human review and auditability.
• Strengthened data ingestion, enrichment, entity resolution, retrieval quality and feedback loops.
• Shipped AI capabilities that demonstrate measurable customer adoption, decision value or workflow improvement.

Requirements

Must-Have

• English proficiency
• Typically 8+ years building production software, data or AI systems, including substantial experience leading multidisciplinary technical teams. Exceptional evidence of production delivery is more important than a specific tenure threshold.
• A proven record of shipping and operating production AI or data systems, not only prototypes, demonstrations or research projects.
• Practical depth in LLM systems, retrieval-augmented generation, orchestration, embeddings, classification, summarisation, extraction and evaluation.
• Strong experience with data pipelines and cloud data platforms. Experience with GCP, BigQuery, AlloyDB, Vertex AI or comparable platforms is preferred.
• Experience designing systems for enterprise or sensitive-data environments, including tenant isolation, access controls, auditability, vendor risk and secure model usage.
• The ability to review code, data models, system architecture and evaluation results in depth, and to contribute directly to technical problem-solving when required.
• The ability to explain AI strategy, architecture, quality, cost, risk and roadmap trade-offs to executives, customers and non-technical stakeholders.
• 10 Years Experience

Strong Signals

• You have designed evaluation, quality-control or regression systems for AI products.
• You have built or governed agentic workflows with explicit permissions, stopping conditions, validation gates and human escalation.
• You have managed model quality, cost and latency trade-offs in production.
• You have worked closely with Product to turn ambiguous customer workflows into measurable, shippable AI features.
• You have improved retrieval quality, entity resolution, classification or structured extraction over messy real-world datasets.
• You have led teams from manual prompting and experimentation toward repeatable, observable and governed AI workflows.
• You have supported enterprise customer conversations involving AI reliability, security, architecture or limitations.

This Role Is Not

• A research leadership position focused primarily on model novelty, publication or benchmark performance.
• A strategy-only role removed from production systems, technical decisions and measurable delivery.
• A prompt-engineering or prototype role without accountability for evaluation, security, reliability, cost and operational ownership.

The Core Question

How do we turn fragmented company, public and customer data into reliable AI-powered stakeholder intelligence, ship it safely, measure its value, and scale it without compromising cost, compliance or engineering velocity?

Why Join TSC

Genie is already used by enterprise teams to understand complex stakeholder, regulatory and reputational environments. The next stage is to make its intelligence layer more reliable, adaptive, explainable and deeply embedded in customer decision workflows.

You will inherit a live product, valuable proprietary data foundations and real enterprise use cases. You will have the mandate to determine how TSC applies LLMs, specialised models, retrieval, agents and structured intelligence systems at production scale, while building the team and quality systems required to make that capability differentiated and defensible.