Applied AI Engineer – Engineering Intelligence

As an AI Engineer specialising in engineering simulation intelligence, you will design and deploy intelligent agent-based systems that integrate with CAE environments. You will work at the intersection of AI, simulation engineering, and data platforms to automate workflows, improve decision accuracy, and unlock insights from large-scale simulation data.

This is a highly cross-functional role involving collaboration with simulation engineers, software teams, and data scientists.

Responsibilities

Agentic AI System Development

• Design and deploy multi-agent AI systems to orchestrate simulation workflows end-to-end
• Build LLM-powered agents with capabilities such as planning, memory, and tool usage
• Develop scalable agent orchestration pipelines using frameworks like LangGraph, AutoGen, CrewAI, or similar

Integration & Engineering Systems

• Integrate AI agents with simulation tools (e.g., meshing, solvers, data systems)
• Connect with external APIs, databases, and internal engineering platforms
• Build production-ready AI systems for real-world engineering environments

RAG & Knowledge Systems

• Develop Retrieval-Augmented Generation (RAG) pipelines using simulation data and technical documentation
• Implement vector databases and embedding models for domain-specific knowledge retrieval

Performance & Reliability

• Monitor, debug, and optimise agent performance, latency, and cost
• Define evaluation frameworks to measure accuracy, reliability, and safety of AI decisions
• Implement guardrails to mitigate hallucination and failure scenarios

Cross-Functional Collaboration

• Work closely with CAE and mechanical engineers to translate requirements into AI solutions
• Communicate complex AI concepts clearly to non-AI stakeholders

Education

• Bachelor’s or Master’s in Computer Science, AI, Data Science, or related field

Experience

• 2–5 years of hands-on experience in AI/ML or applied AI engineering
• Experience building end-to-end AI systems (not just experimentation)
• Exposure to LLMs and AI agents in production environments

Technical Skills (Must-Have)

• Strong Python programming skills
• Experience with LLMs (OpenAI, open-source models, etc.)
• Understanding of agent-based systems and tool integration
• Experience with APIs, microservices, and system integration
• Familiarity with cloud platforms (preferably GCP)
• Knowledge of software engineering best practices (testing, version control)

Preferred Skills (Good to Have)

• Experience with agent frameworks (LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel)
• Knowledge of RAG architectures and vector databases (Pinecone, ChromaDB, etc.)
• Familiarity with MLOps tools (Docker, CI/CD, model serving frameworks)
• Experience with structured outputs and function calling
• Exposure to CAE/FEA tools (ANSYS, Abaqus, LS-DYNA)

Core Competencies

• Agentic system design (planning, memory, orchestration)
• Prompt engineering and LLM optimisation
• Reliability engineering and AI safety practices
• Strong analytical thinking and problem-solving
• Effective cross-functional communication