Home › Companies › Efds Fa Em5 Oraclecloud Com CX 1 › Applied AI Engineer – Engineering Intelligence
Applied AI Engineer – Engineering Intelligence
Efds Fa Em5 Oraclecloud Com CX 1 · GTBC Office Building 02, Chennai, TN, IN · Hybrid · Active · Oracle Recruiting Cloud / Fusion HCM
Job facts
| Field | Value |
|---|---|
| Company | Efds Fa Em5 Oraclecloud Com CX 1 |
| Title | Applied AI Engineer – Engineering Intelligence |
| Normalized title | - |
| Department / team | PD Operations and Quality |
| Location | Chennai, TN, United States |
| Work model | Hybrid / Hybrid |
| Employment type | Full Time |
| Salary | - |
| Status | active |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
| Posted / first seen | 2026-06-22 / 2026-06-22 |
| Changed / last seen | 2026-06-22 / 2026-06-22 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Efds Fa Em5 Oraclecloud Com CX 1. | Open |
| Company breakdowns | Role, location, ATS, and work model facets for this company. | Open |
| ATS provider jobs | Active postings observed through Oracle Recruiting Cloud / Fusion HCM. | Open |
| Provider filtered search | The same provider as a filtered job collection. | Open |
| City jobs | Active postings in Chennai. | Open |
| Department jobs | Active postings in PD Operations and Quality. | Open |
| Work model jobs | Active Hybrid postings. | Open |
| Lifecycle events | Open, update, close, and reopen events for this posting. | Open |
| Original posting | Canonical source or apply URL captured from the ATS. | Open |
Linked records
| Company | Efds Fa Em5 Oraclecloud Com CX 1 |
| Source | d8110a61-5510-417b-a74c-f58816339c6b |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
Description
Description
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
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
Qualifications
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
Full job record
| Job ID | ac798c4b12d5bcc805d4463ace32cec3bb97da61 |
| Org ID | fc791186-3bfa-4e9d-8648-0d2f6f66937d |
| Source ID | d8110a61-5510-417b-a74c-f58816339c6b |
| Board ID | d8110a61-5510-417b-a74c-f58816339c6b |
| Provider | oracle_hcm |
| Provider Job Key | 65008 |
| Title | Applied AI Engineer – Engineering Intelligence |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | GTBC Office Building 02, Chennai, TN, IN |
| Department | PD Operations and Quality |
| Team | — |
| Employment Type | full_time |
| Workplace Type | hybrid |
| Remote Policy | hybrid |
| Country | United States |
| Region | TN |
| City | Chennai |
| Salary Raw | Description 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 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 Qualifications 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 |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/65008 |
| Apply URL | https://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/65008 |
| First Seen At | 2026-06-22 15:13:30Z |
| Last Seen At | 2026-06-22 15:13:30Z |
| Last Checked At | 2026-06-22 15:13:30Z |
| Last Changed At | 2026-06-22 15:13:30Z |
| Inactive At | — |
| Source Posted At | 2026-06-22 06:22:41Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=oracle_hcm/board=efds.fa.em5.oraclecloud.com|CX_1/date=2026-06-22/2026-06-22T15-12-07-505Z-fb3c57bc435c7742642ab6cc7b955e4c447b2e3360433c2382ef3fca17c9b32c.json |
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