Secure AI Platform Architect & MLOps Leader

PEOPLESEARCH PTE. LTD.

Singapore

On-site

SGD 120,000 - 180,000

Full time

14 days+

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Job summary

PEOPLESEARCH PTE. LTD. seeks an AI Platform Engineer to design, secure, and scale enterprise-grade AI platforms and end-to-end ML workflows.

You will architect pipelines spanning data ingestion, model orchestration, and API orchestration across hybrid cloud and on-premise environments, with emphasis on security-by-design and privacy-preserving controls. You will embed Responsible AI, RBAC, and compliance policies, while guiding PoCs and aligning architecture with business goals in a complex

Qualifications

Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Engineering, Data Science, AI/ML, or related field.
3-5+ years of experience in enterprise architecture, cybersecurity, secure systems engineering, or AI/ML platform integration.
Certifications in major cloud platforms (AWS, Azure, GCP), Databricks, or Security Architecture (advantageous).
Hands-on experience with AI pipeline design, model serving APIs, vector databases, agentic frameworks, and LLM orchestration.
Proficiency with major cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI) and big data/container engines.
Knowledge of OWASP Top 10 for LLMs, Zero Trust frameworks, DevSecOps practices, RBAC, and secrets management.
Data privacy controls (encryption, tokenization, anonymization) and regulatory compliance requirements.

Responsibilities

End-to-End Solution Architecture: Design and deploy enterprise-grade AI workflows spanning data pipelines, model orchestration, API integration, and serving layers across hybrid cloud and on-premise environments.
Core AI Services & Runtime: Architect shared core services for the central AI platform, including Agentic AI frameworks, AI workbenches, MCP, shared RAG capabilities, and AI runtime environments.
Integration & Orchestration: Integrate AI systems with enterprise platforms and APIs, leveraging LangChain, LangGraph, vector databases.
Standards & Reference Patterns: Define blueprints, reusable design patterns, and reference implementations to streamline AI deployment.
AI Security, Risk & Governance: Embed security, privacy, and compliance principles into AI platforms, pipelines, and deployment frameworks.
Engineering Operations & Platform Strategy: Evaluate and integrate platform and security toolkits; oversee VAPT for models and integrations.