Applied AI Engineer

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