Job Title: AI Engineer
Department: Technology & Digital Transformation
Reports to: Chief Technology Officer (CTO)
(DLP-supported, commensurate with experience)
Job Grade: Senior Technical Specialist
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 optimize 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.
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