IT Application Support Specialist L2 (Agentic AI Engineer)

BASIL TECHNOLOGIES PTE. LTD.; Singapore; On-site; SGD 180,000 - 260,000; Full time

Job summary

Basil Technologies Pte. Ltd. is seeking an experienced AI/ML engineer to lead Gemini Enterprise agent development. You will design, implement, and operate multi-agent systems using Gemini Enterprise, Vertex AI, and Google Cloud tools.

You will own grounding, data connectors, enterprise search, RAG pipelines, and governance controls to prevent leakage and hallucinations. Collaboration with security, legal, and data teams is essential.

Qualifications

• 7+ years of AI/ML engineering or Gen AI platform experience.
• Hands-on with Gemini Enterprise (agent design, grounding, data connectors).
• Experience implementing AI governance controls and audit trails.
• Strong Python programming and automation skills.
• Familiarity with cloud platforms and DevOps practices.

Responsibilities

• Design, build, and own multi-agent solutions on Gemini Enterprise.
• Configure grounding, enterprise search, and data connectors.
• Integrate Gemini models via API/Vertex AI into agents and apps.
• Design and productionize RAG pipelines and retrieval strategies.
• Evaluate agent quality with governance and cost considerations.
• Define governance frameworks and guardrails for production agents.
• Monitor, log, and audit agent behavior and policy compliance.
• Collaborate with security, legal, and compliance stakeholders.

Skills

AI/ML engineering

Gen AI platforms

Agent/LLM development

Governance & policy

Python programming

Cross-functional collaboration

Ownership mindset

Tools

Gemini Enterprise

Vertex AI

GCP (IAM, Storage)

Terraform

Kubernetes (GKE)

CI/CD pipelines

API integration

Monitoring stacks

Job description

Key Responsibilities

Gemini Enterprise Agent Development

• Design, build, and own multi-agent solutions on Gemini Enterprise (formerly Agent space) — agent 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 the Gemini API and/or Vertex AI) into agents and downstream applications, tuning prompts, context strategies, and tool/function calling for reliability.
• Design and productionize RAG pipelines and retrieval strategies that feed agent grounding sources.
• Continuously evaluate and iterate on agent quality — accuracy, relevance, latency, and cost — using structure devaluation frameworks.

AI Governance & Responsible Agent Operations

• Define and implement governance frameworks for Gemini Enterprise agents: access control, data permissions, usage policies, and approval workflows for new agents going into production.
• Build guardrails against hallucination, data leakage, prompt injection, and unauthorized data access across agents and connectors.
• Establish monitoring, logging, and audit trails for agent behavior, including token usage, response quality, and policy violations.
• Partner with security, legal, and compliance stakeholders to ensure agents meet data privacy, residency, and responsible-AI requirements.
• Create and maintain documentation, review checklists, and lifecycle standards (build → evaluate → approve →monitor → retire) for enterprise agents.
• Track Gemini model and Gemini Enterprise feature releases and assess their impact on existing agents and governance policies.

Cross-Functional Collaboration & Automation

• Work closely with data science, AI engineering, security, and business teams to translate use cases into governed, production-ready agents.
• Automate agent configuration, evaluation, and deployment workflows using Python and APIs/SDKs for Gemini Enterprise and Vertex AI.
• Build internal tooling and dash boards to give stakeholders visibility into agent inventory, usage, and governance status.
• Participate in code and design reviews, contributing to shared standards for agent development and governance.

Required Skills & Experience

• 7+ years of experience in AI/ML engineering, applied AI, or Gen AI platform roles, with hands‑on ownership of agent or LLM-application development.
• Direct, hands‑on experience building and configuring agents on Gemini Enterprise (or Agent space) — agent design, grounding, enterprise search, data connectors.
• Strong hands‑on experience with Gemini models (via 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 for LLM/agent systems.
• Solid understanding of LLM application patterns — RAG, embeddings, vector search, multi‑agent orchestration.
• Solid Python programming skills for automation, evaluation tooling, and API integration.
• Ability to work cross‑functionally with data science, security/compliance, and business stakeholders to govern and scale agent deployments.
• Working familiarity with Google CloudPlatform (GCP) — IAM, Cloud Storage, basic networking — sufficient to collaborate with platform/infrastructure teams.

Preferred / Nice-to-Have

• Google Cloud certifications(Professional Machine Learning Engineer, or Professional Cloud Architect).
• Experience with Terraform, Kubernetes(GKE), or CI/CD pipelines, for coordinating with platform/DevOps teams on agent infrastructure.
• Experience with monitoring/observability stacks (Cloud Monitoring, Prometheus, Grafana, Datadog).
• Familiarity with responsible-AI/model-risk frameworks applied to enterprise GenAI.
• Prior experience with other enterprise Gen AI /agent platforms (e.g., OpenAI, Anthropic, open-source LLM stacks) as a point of comparison.

Soft Skills

• Strong governance and risk mindset —able to balance agent capability with safety, compliance, and trust.
• Clear communicator who can translate technical agent behavior into business and compliance language.
• Comfortable operating in ambiguity, especially with fast-evolving Gemini features and emerging agent governance practices.
• Ownership mentality — from agent design through deployment, evaluation, and long-term governance.

Tech Stack Summary

• Agent Development (Primary): Gemini Enterprise (Agent space), 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): Google Cloud Platform (GCP) — IAM, Cloud Storage, networking basics
• Monitoring: Cloud Monitoring, Cloud Logging, Prometheus, Grafana