AI Engineer (DLP)

EVERSAFE ACADEMY PTE. LTD.; Dublin; On-site; EUR 70,000 - 90,000; Full time

Job summary

EVERSAFE ACADEMY PTE. LTD. is seeking a skilled AI Engineer to lead their digital transformation under the IMDA Digital Leaders Programme. This role will involve developing sophisticated AI agents and advanced RAG architectures, optimizing Machine Learning models, and ensuring compliance and security in AI applications.

The ideal candidate will have at least 3 years of experience in AI/Machine Learning Engineering, with strong technical expertise and a production-first mindset. A Bachelor's or Master's in Computer Science or a related field is required.

Qualifications

• Minimum of 3 years in AI/Machine Learning Engineering.
• At least 1 year in Generative AI production environments.
• Deep hands-on experience with orchestration frameworks.

Responsibilities

• Design and deploy multi-agent systems for complex business processes.
• Develop high-precision Retrieval-Augmented Generation pipelines.
• Architect integrations between LLMs and third-party platforms.
• Implement traditional Machine Learning models to complement GenAI features.
• Establish rigorous benchmarking for agent reliability and accuracy.
• Collaborate with Data Engineers for data layer optimisation.

Skills

AI/Machine Learning Engineering

Generative AI

Python

API design skills

Asynchronous programming

Education

Bachelor's or Master's degree in Computer Science, AI

Tools

PyTorch

TensorFlow

LangGraph

LlamaIndex

Pinecone

Weaviate

Milvus

DeepEval

Ragas

LangSmith

AWS Bedrock

Azure AI Foundry

Vertex AI

Job description

Position Summary

The AI Engineer will be a cornerstone of Eversafe Academy’s digital transformation under the IMDA Digital Leaders Programme (DLP). Reporting directly to the CTO, you will lead the architecture and deployment of autonomous AI agents designed to revolutionise safety training analytics and operational compliance. This is not a "wrapper" development role; you will build sophisticated, multi-step agentic workflows that integrate deeply with enterprise data layers to drive real-time decision‑making.

Key Responsibilities

• Agentic Workflow Orchestration: Design and deploy multi‑agent systems capable of autonomous reasoning, task decomposition, and tool use to automate complex business processes.
• Advanced RAG Architectures: Develop and optimise high‑precision Retrieval‑Augmented Generation (RAG) pipelines, utilising hybrid search and re‑ranking to ensure 100% grounding in regulatory and technical documentation.
• System Integration & Tool Use: Architect seamless integrations between LLMs and internal APIs/third‑party platforms (WhatsApp, CRM, LMS) using MCP and A2A protocols.
• Model Development & Optimisation: Design and implement traditional Machine Learning models (anomaly detection, classification, and regression) to complement GenAI features.
• AI Safety & Governance: Implement robust "Human‑in‑the‑loop" (HITL) workflows, prompt‑injection guardrails, and PII masking to ensure secured and compliant agentic behavior.
• Evaluation & Observability: Establish rigorous benchmarking to evaluate, monitor and improve agent reliability, latency, and reasoning accuracy.
• Cross‑Functional Collaboration: Partner with Data Engineers to ensure the underlying data layer is optimised for high‑speed retrieval and agentic consumption.

Qualifications & Experience

• Education: Bachelor’s or Master’s degree in Computer Science, AI, or a related field.
• Experience: Minimum of 3 years in AI/Machine Learning Engineering, with at least 1 year of dedicated experience in Generative AI production environments.
• Agentic Expertise: Deep hands‑on experience with orchestration frameworks: LangGraph, LlamaIndex, or CrewAI.
• Technical Stack:
  * Models: Experience with frontier models (GPT‑5, Claude 4.6) and fine‑tuning
  open‑source models (Llama 4, Mistral).
  * Vector Infrastructure: Proficiency in Pinecone, Weaviate, or Milvus.
  * Protocols: Familiarity with Model Context Protocol (MCP) and
  Agent‑to‑Agent (A2A) communication patterns.
  * ML: Experience with frameworks (PyTorch or TensorFlow)
• Evaluation Tools: Proven experience with DeepEval, Ragas, or LangSmith for systematic testing.
• Engineering Excellence:
  * Expert‑level Python and experience with asynchronous programming.
  * Strong API design skills (REST/GraphQL).
  * Familiarity with Cloud AI Orchestrators (AWS Bedrock, Azure AI Foundry,
  or Vertex AI).

Mindset

A "production‑first" mentality—understanding that an AI demo is easy, but a reliable AI system is hard.