AI Platform Engineer (Architecture & Security)

PEOPLESEARCH PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

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.

Skills

AI/ML architecture

Security engineering

Platform integration

Cloud platforms

RBAC & access controls

Education

Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Engineering, Data Science, AI/ML, or related

Tools

Databricks

AWS Guardrails

Azure Responsible AI

LangChain

LangGraph

MCP

SageMaker

Vertex AI

Job description

Position Summary

As an AI Platform Engineer, you will be responsible for designing, securing, and scaling enterprise-grade AI platforms and end-to-end Machine Learning workflows. Sitting at the intersection of system architecture, security engineering, and MLOps, this role ensures our central AI ecosystem is secureby-design, scalable, and business-aligned. You will architect robust end-to-end pipelines—ranging from agentic AI workflows, Retrieval-Augmented Generation (RAG) services, and LLM runtime environments to data ingestion and API orchestration. Concurrently, you will embed cybersecurity controls, privacy-preserving mechanisms, Responsible AI (RAI) frameworks, and compliance policies into every layer of our hybrid cloud and on-premise AI infrastructure.

Key Responsibilities

1. Platform Architecture & AI Workflow Design

• End-to-End Solution Architecture: Design and deploy enterprisegrade 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, Model Context Protocol (MCP), shared RAG capabilities, and AI runtime environments.
• Integration & Orchestration: Integrate AI systems with enterprise platforms and APIs, leveraging advanced orchestration tools (e.g., LangChain, LangGraph, vector databases).
• Standards & Reference Patterns: Define architectural blueprints, reusable design patterns, and reference implementations to streamline AI deployment across different business units.

2. AI Security, Risk & Governance

• Secure-by-Design Architecture: Embed security, data privacy, and compliance principles into AI platforms, data pipelines, and deployment frameworks.
• Threat Modeling & Risk Assessments: Conduct AI-specific threat modeling and risk evaluations addressing model misuse, data leakage, prompt injection vulnerabilities, adversarial attacks, and LLM security.
• Data Privacy & Controls: Implement privacy-preserving techniques into AI workflows, including data anonymization, tokenization, encryption in transit/at rest, role-based access controls (RBAC), and secure logging.
• Responsible & Explainable AI (RAI/XAI): Establish frameworks for Responsible AI and Explainable AI to ensure model decisions remain transparent, interpretable, ethical, and aligned with governance policies.

3. Engineering Operations & Platform Strategy

• Tooling Evaluation: Evaluate, recommend, and integrate platform and security toolkits (e.g., Databricks, AWS Guardrails, Azure Responsible AI, open-source AI frameworks).
• Vulnerability & Testing Oversight: Coordinate and approve Vulnerability Assessment and Penetration Testing (VAPT) for both inhouse models and third-party/open-source AI integrations.
• Prototyping & Leadership: Drive technical Proofs of Concept (PoCs), vendor technology reviews, and innovation pilots while guiding small engineering teams through implementation.

Skills for Success

Qualifications & Experience

• Bachelor’s or Master’s degree in Computer Science, Cybersecurity, Engineering, Data Science, AI/ML, or a related technical discipline.
• 3-5+ years of experience in enterprise architecture, cybersecurity, secure systems engineering, or AI/ML platform integration.
• Certifications (Advantageous): Certifications in major cloud platforms (AWS, Azure, GCP), Databricks, or Security Architecture.

Technical Skills

• AI/ML Architecture & Frameworks: Hands-on experience with AI pipeline design, model serving APIs, vector databases, agentic frameworks, and LLM orchestration (e.g., LangChain, LangGraph, MCP).
• Cloud & Platform Ecosystems: Proficiency with major cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI) and big data/container engines.
• AI Security & Governance: Knowledge of OWASP Top 10 for LLMs, Zero Trust frameworks, DevSecOps practices, RBAC, and secrets management.
• Data Protection & Compliance: Strong technical understanding of data privacy controls (encryption, tokenization, anonymization) and regulatory compliance requirements.