Procurement Manager

People Profilers Pte Ltd; Singapore; On-site; SGD 180,000 - 240,000; Full time

Job summary

People Profilers Pte Ltd is seeking a senior AI/GenAI platform engineer to lead design, development, and governance of enterprise AI agents built on Gemini Enterprise. You will own the full agent lifecycle from architecture to deployment and ongoing governance on Google Cloud Platform.

You will integrate Gemini models (via Gemini API/Vertex AI), tune prompts, design RAG pipelines, and ensure agents are accurate, safe, and compliant for production use.

Qualifications

• 7+ years in AI/ML engineering or GenAI platform roles with hands-on ownership of agent or LLM-application development

Responsibilities

• Design, build, and own multi-agent solutions on Gemini Enterprise, covering architecture, orchestration, task/tool design, and multi-turn conversation flows
• Configure grounding, enterprise search, and data connectors for accurate, up-to-date information in agents
• Integrate Gemini models (via Gemini API/Vertex AI) into agents and downstream apps, tuning prompts and context strategies
• Design and productionize RAG pipelines and retrieval strategies for agent grounding
• Evaluate and iterate on agent quality (accuracy, relevance, latency, cost) using evaluation frameworks
• Define governance frameworks: access control, data permissions, usage policies, and approval workflows for new production agents
• Build guardrails against hallucination, data leakage, prompt injection, and unauthorized data access
• Establish monitoring, logging, and audit trails for agent behavior and policy violations
• Partner with security, legal, and compliance teams on data privacy and responsible-AI requirements
• Create and maintain documentation, checklists, and lifecycle standards (build → evaluate → approve → monitor → retire)
• Track Gemini model and Gemini Enterprise releases and assess impact on agents and governance policies
• Collaborate with data science, AI engineering, security, and business teams to turn use cases into governed, production-ready agents
• Automate agent configuration, evaluation, and deployment workflows using Python and Gemini Enterprise/Vertex AI APIs and SDKs
• Build internal tooling and dashboards for visibility into agent inventory, usage, and governance status
• Participate in code and design reviews and contribute to shared standards for agent development and governance

Skills

AI/ML engineering

Gemini Enterprise

Python automation

Cross-functional collaboration

GCP familiarity

Gemini API

Tools

Gemini API

Vertex AI

Terraform

Kubernetes (GKE)

CI/CD pipelines

Job description

Role Summary

• Lead the design, development, and governance of enterprise AI agents built on Gemini Enterprise and Gemini models

• Own the full agent lifecycle: architecture, grounding, deployment, evaluation, and ongoing governance

• Ensure agents are accurate, safe, compliant, and trusted enough for production use

• Serve as the go-to expert for building agents on Gemini Enterprise and for setting up the governance frameworks that keep them reliable at scale

• Collaborate with the GCP platform team (the underlying infrastructure runs on Google Cloud Platform)

Key Responsibilities

Gemini Enterprise Agent Development

• Design, build, and own multi-agent solutions on Gemini Enterprise (formerly Agentspace), covering architecture, orchestration, task/tool design, and multi-turn conversation flows

• Configure grounding, enterprise search, and data connectors so agents retrieve accurate, up-to-date, and properly scoped information

• Integrate Gemini models (via Gemini API and/or Vertex AI) into agents and downstream applications, tuning prompts, context strategies, and tool/function calling

• Design and productionize RAG pipelines and retrieval strategies for agent grounding

• Evaluate and iterate on agent quality (accuracy, relevance, latency, cost) using structured evaluation frameworks

AI Governance & Responsible Agent Operations

• Define and implement governance frameworks: access control, data permissions, usage policies, and approval workflows for new production agents

• Build guardrails against hallucination, data leakage, prompt injection, and unauthorized data access

• Establish monitoring, logging, and audit trails for agent behavior, including token usage, response quality, and policy violations

• Partner with security, legal, and compliance teams to meet data privacy, residency, and responsible-AI requirements

• Create and maintain documentation, review checklists, and lifecycle standards (build → evaluate → approve → monitor → retire)

• Track Gemini model and Gemini Enterprise releases and assess their impact on existing agents and governance policies

Cross-Functional Collaboration & Automation

• Work with data science, AI engineering, security, and business teams to turn use cases into governed, production-ready agents

• Automate agent configuration, evaluation, and deployment workflows using Python and Gemini Enterprise/Vertex AI APIs and SDKs

• Build internal tooling and dashboards for visibility into agent inventory, usage, and governance status

• Participate in code and design reviews and contribute to shared standards for agent development and governance

Required Skills & Experience

• 7+ years in AI/ML engineering, applied AI, or GenAI platform roles, with hands-on ownership of agent or LLM-application development

• Direct, hands-on experience building and configuring agents on Gemini Enterprise (or Agentspace): agent design, grounding, enterprise search, data connectors

• Strong hands-on experience with Gemini models (Gemini API or Vertex AI): prompting, tool/function calling, context and RAG design

• Practical experience implementing AI governance controls: access management, guardrails, evaluation frameworks, audit logging, and responsible-AI policies

• Solid understanding of LLM application patterns: RAG, embeddings, vector search, multi-agent orchestration

• Solid Python skills for automation, evaluation tooling, and API integration

• Ability to work cross-functionally with data science, security/compliance, and business stakeholders

• Working familiarity with GCP (IAM, Cloud Storage, basic networking)

Preferred / Nice-to-Have

• Google Cloud certifications (Professional Machine Learning Engineer or Professional Cloud Architect)

• Experience with Terraform, Kubernetes (GKE), or CI/CD pipelines

• Experience with monitoring/observability tools (Cloud Monitoring, Prometheus, Grafana, Datadog)

• Familiarity with responsible-AI and model-risk frameworks for enterprise GenAI

• Experience with other enterprise GenAI/agent platforms (e.g., OpenAI, Anthropic, open-source LLM stacks) for comparison

Soft Skills

• Strong governance and risk mindset, balancing agent capability with safety, compliance, and trust

• Clear communicator who can translate technical agent behavior into business and compliance language

• Comfortable with ambiguity amid fast-evolving Gemini features and emerging governance practices

• Ownership mentality, from agent design through deployment, evaluation, and long-term governance

Tech Stack

• Agent Development: Gemini Enterprise (Agentspace), Gemini models, Vertex AI, Model Armor, custom ADK agents, Agent Designer

• AI Governance: Evaluation frameworks, audit/logging tooling, responsible-AI guardrails, IAM/access policies

• Retrieval & Data: RAG pipelines, enterprise search, data connectors, vector search

• Languages: Python (primary), Terraform, JavaScript, Bash

• Cloud Platform (supporting): GCP (IAM, Cloud Storage, networking basics)

• Monitoring: Cloud Monitoring, Cloud Logging, Prometheus, Grafana