# Director of AI & Data Platform

**Company:** [OPSWAT](https://hotfix.jobs/companies/opswat)
**Location:** Ho Chi Minh City, Vietnam
**Role:** Engineering Management
**Experience:** 10+ years
**Skills:** LLMs, Retrieval-Augmented Generation, LangGraph, LangChain, Mcp, Prompt Engineering, Model Evaluation, Model Observability, Private Llms, Supervised Fine-Tuning, Quantization, Gpu Infrastructure, Kubernetes, vLLM, Sglang
**Posted:** 2026-08-06

> Leads the AI Platform & Data organization, owning private LLM infrastructure, agent orchestration, data platforms, analytics, and governance. Requires 10+ years in software, data, or platform engineering, substantial engineering leadership experience, and hands-on expertise across modern AI and data stacks.

## Job Description

## Responsibilities
- Own the strategy, roadmap, and execution for the AI Platform & Data pillar, including MCP servers, agent runtime and orchestration, AI-native data platforms, analytics products, and governance.
- Lead a multi-team organization spanning orchestration, data engineering, analytics, and governance; recruit, mentor, and develop engineering managers and technical leads.
- Build an AI-native data platform with CDC pipelines, LLM-ready semantic layers, lineage, freshness SLAs, and data-quality frameworks.
- Reinvent analytics through natural-language analytics, autonomous deep dives, anomaly detection, and proactive business-signal surfacing.
- Define architectural patterns for AI-powered internal workflows across Finance, HR, Legal, CX, Supply Chain, and GTM Systems.
- Own the private LLM strategy, including model selection, fine-tuning and post-training, secure deployment, inference infrastructure, evaluation, observability, and lifecycle management.
- Establish workload-routing decisions between private and approved external foundation models based on data classification, security, latency, quality, and cost.
- Govern training and evaluation data pipelines, including curation, anonymization, access controls, versioning, benchmarking, red-team testing, and data-leakage prevention.
- Partner with Security, Engineering, Legal, and Product on private-model use cases.
- Build MCP servers, data connectors, and agent primitives that reduce shadow IT.
- Establish governance for model access, data classification, audit, evaluation, and cost controls.
- Drive AI-assisted SDLC, agentic coding, and internal-tooling velocity.
- Represent the pillar to executive leadership, business-function heads, and external partners.

## Requirements
- 10+ years of experience in software, data, or platform engineering.
- 6+ years leading engineering teams and at least 2 years managing managers.
- Track record building and shipping platform products at scale, such as internal developer platforms, AI/ML infrastructure, or modern data platforms.
- Fluency with LLMs, RAG, agent frameworks, MCP, prompt engineering, and evaluation/observability.
- Experience with private LLM deployment and optimization, including open-weight models, supervised fine-tuning, preference optimization, quantization, inference serving, GPU infrastructure, model evaluation, and secure model operations.
- Experience designing hybrid AI architectures that route requests between private and commercial foundation models.
- Strong data engineering and analytics foundation, including lakehouse architectures, CDC, streaming, dbt-style transformation, semantic layers, and analytics-grade data quality.
- Experience building systems that pass security audits.
- Ability to partner with non-technical business functions and translate ambiguous requirements into platform capabilities.
- Excellent written and verbal communication.

## Nice to Have
- Cybersecurity, critical-infrastructure, or regulated-industry experience.
- Experience fine-tuning or post-training open-weight models for enterprise, cybersecurity, software engineering, or domain-specific use cases.
- Familiarity with vLLM, SGLang, Hugging Face, NVIDIA NIM, Kubernetes-based GPU serving, or equivalent tools.
- Experience automating revenue operations, finance, HR, or legal workflows through AI.
- Familiarity with SugarCRM, Salesforce, or comparable enterprise-system metadata and integration patterns.
- Experience deploying MCP servers, agent infrastructure, or AI gateways in production.
- Perspective on how AI is changing software delivery for internal engineering teams.

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