Agentic AI Engineer - 12 months contract

PERSOL TECH SERVICES PTE. LTD.; Singapore; Vor Ort; SGD 120.000 - 180.000; Vollzeit

Zusammenfassung

PERSOL TECH SERVICES PTE. LTD. is seeking an experienced AI/ML engineer to design and implement enterprise agents on Gemini Enterprise.

You will own agent architecture, grounding, data connectors, and multi-turn conversations, integrating Gemini models via API or Vertex AI and optimizing RAG pipelines. You will work with security, legal, and compliance stakeholders to ensure responsible-AI practices, governance, and auditable workflows while collaborating across data science and engineering

Qualifikationen

• Bachelor’s degree in CS/IT/SE or related field.
• 7+ years in AI/ML engineering or GenAI with hands-on agent work.
• Hands-on building/configuring agents on Gemini Enterprise (Agentspace).
• Strong experience with Gemini models via API or Vertex AI.
• Practical AI governance: access controls, guardrails, audit logs.
• Understanding of LLM patterns: RAG, embeddings, vector search.
• Strong Python for automation, tooling, and integration.
• Collaborates with data science, security, and business teams.
• Familiar with GCP IAM, Storage, networking for collaboration.
• Terraform, Kubernetes (GKE), CI/CD experience.
• Monitoring stacks: Prometheus, Grafana, Datadog.

Aufgaben

• Design, build, and own multi-agent solutions on Gemini Enterprise.
• Configure grounding, enterprise search, and data connectors.
• Integrate Gemini models into agents and apps via API/Vertex AI.
• Design and productionize RAG pipelines and retrieval strategies.
• Continuously evaluate agent quality: accuracy, latency, cost.

Kenntnisse

AI/ML engineering

GenAI platforms

Gemini Enterprise

Grounding & RAG

Python

Terraform/Kubernetes

GCP

Security & governance

Communication

CI/CD

Ausbildung

Bachelor's degree in CS/IT/SE

Tools

Gemini API

Vertex AI

Prometheus

Grafana

Datadog

Kubernetes

GKE

Jobbeschreibung

Duration: 1 year

Working Hours: 8.30am - 6pm, Monday - Fridays. Standby and shift work may be required

Location: Central

Key Responsibilities

• Design, build, and own multi-agent solutions on Gemini Enterprise (formerly Agentspace) — 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 structured evaluation 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 dashboards 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.

Qualifications

• Bachelor's degree in Computer Science, Information Technology, Software Engineering or a related discipline.

• 7+ years of experience 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 (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 Cloud Platform (GCP) — IAM, Cloud Storage, basic networking — sufficient to collaborate with platform/infrastructure teams.

• 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 GenAI/agent platforms (e.g., OpenAI, Anthropic, open‑source LLM stacks) as a point of comparison

• 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.