Applied AI Engineer – Engineering Intelligence

Job ID

65008

Category

PD Operations and Quality

Location

Chennai, India

Work Type

Hybrid

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