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HomeCompaniesEfds Fa Em5 Oraclecloud Com CX 1Applied 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

FieldValue
CompanyEfds Fa Em5 Oraclecloud Com CX 1
TitleApplied AI Engineer – Engineering Intelligence
Normalized title-
Department / teamPD Operations and Quality
LocationChennai, TN, United States
Work modelHybrid / Hybrid
Employment typeFull Time
Salary-
Statusactive
ATS providerOracle Recruiting Cloud / Fusion HCM
Posted / first seen2026-06-22 / 2026-06-22
Changed / last seen2026-06-22 / 2026-06-22

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PageWhat it containsOpen
Company jobsActive postings from Efds Fa Em5 Oraclecloud Com CX 1.Open
Company breakdownsRole, location, ATS, and work model facets for this company.Open
ATS provider jobsActive postings observed through Oracle Recruiting Cloud / Fusion HCM.Open
Provider filtered searchThe same provider as a filtered job collection.Open
City jobsActive postings in Chennai.Open
Department jobsActive postings in PD Operations and Quality.Open
Work model jobsActive Hybrid postings.Open
Lifecycle eventsOpen, update, close, and reopen events for this posting.Open
Original postingCanonical source or apply URL captured from the ATS.Open

Linked records

CompanyEfds Fa Em5 Oraclecloud Com CX 1
Sourced8110a61-5510-417b-a74c-f58816339c6b
ATS providerOracle 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 IDac798c4b12d5bcc805d4463ace32cec3bb97da61
Org IDfc791186-3bfa-4e9d-8648-0d2f6f66937d
Source IDd8110a61-5510-417b-a74c-f58816339c6b
Board IDd8110a61-5510-417b-a74c-f58816339c6b
Provideroracle_hcm
Provider Job Key65008
TitleApplied AI Engineer – Engineering Intelligence
Normalized Title
Statusactive
Activeyes
Location TextGTBC Office Building 02, Chennai, TN, IN
DepartmentPD Operations and Quality
Team
Employment Typefull_time
Workplace Typehybrid
Remote Policyhybrid
CountryUnited States
RegionTN
CityChennai
Salary RawDescription 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 URLhttps://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/65008
Apply URLhttps://efds.fa.em5.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_1/job/65008
First Seen At2026-06-22 15:13:30Z
Last Seen At2026-06-22 15:13:30Z
Last Checked At2026-06-22 15:13:30Z
Last Changed At2026-06-22 15:13:30Z
Inactive At
Source Posted At2026-06-22 06:22:41Z
Source Updated At
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