Home › Companies › Fa Ertb Saasfaprod1 Fa Ocs Oraclecloud Com CX 2 › Sr Data Scientist - Gen AI ML - Irving
Sr Data Scientist - Gen AI ML - Irving
Fa Ertb Saasfaprod1 Fa Ocs Oraclecloud Com CX 2 · United States; Texas, Irving, Texas, US · Active · Oracle Recruiting Cloud / Fusion HCM
Job facts
| Field | Value |
|---|---|
| Company | Fa Ertb Saasfaprod1 Fa Ocs Oraclecloud Com CX 2 |
| Title | Sr Data Scientist - Gen AI ML - Irving |
| Normalized title | - |
| Department / team | Development |
| Location | United States |
| Work model | - |
| Employment type | - |
| Salary | - |
| Status | active |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
| Posted / first seen | 2026-05-13 / 2026-05-31 |
| Changed / last seen | 2026-05-31 / 2026-06-06 |
Related slices
| Page | What it contains | Open |
|---|---|---|
| Company jobs | Active postings from Fa Ertb Saasfaprod1 Fa Ocs Oraclecloud Com CX 2. | 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 |
| Department jobs | Active postings in Development. | 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 | Fa Ertb Saasfaprod1 Fa Ocs Oraclecloud Com CX 2 |
| Source | c9ed31af-e3b7-45ab-9928-44fa8c877305 |
| ATS provider | Oracle Recruiting Cloud / Fusion HCM |
Description
Description
Role Summary:
We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices.
Technical Stack:
LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment.
Frameworks: LangChain, LlamaIndex, and Hugging Face.
Orchestration: LangGraph and Multi-Agent Systems (MAS).
Development: Python, FastAPI, and Asynchronous Programming.
RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies.
ML/DL: PyTorch, TensorFlow, and Model Fine-tuning.
Deployment: Docker, Production API management, and LLM monitoring.
Tools: Prompt Engineering, Workflow Design, and GenAI Optimization.
Key Responsibilities:
Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures.
Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval.
Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements.
Design and deploy high-performance, scalable backend services using FastAPI and Async Python.
Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance.
Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools.
Containerize and deploy AI services via Docker to production environments.
Required Qualifications:
7+ years of experience ; Hands-on experience building and deploying GenAI applications in a production setting.
Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.).
Experience with vector search, embedding models, and advanced data retrieval patterns.
Knowledge of model fine-tuning techniques and local LLM quantization/hosting.
Familiarity with production-grade monitoring, API security, and CI/CD for ML.
Compensation, Benefits and Duration
Minimum Compensation: USD 53,000
Maximum Compensation: USD 188,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is not available for independent contractors
No applications will be considered if received more than 120 days after the date of this post
Full job record
| Job ID | 7306c4ce235f6acf111efcc1faa58625b8681662 |
| Org ID | 9b9010ed-5f3e-4445-bf65-2990847efc8d |
| Source ID | c9ed31af-e3b7-45ab-9928-44fa8c877305 |
| Board ID | c9ed31af-e3b7-45ab-9928-44fa8c877305 |
| Provider | oracle_hcm |
| Provider Job Key | 25962 |
| Title | Sr Data Scientist - Gen AI ML - Irving |
| Normalized Title | — |
| Status | active |
| Active | yes |
| Location Text | United States; Texas, Irving, Texas, US |
| Department | Development |
| Team | — |
| Employment Type | — |
| Workplace Type | — |
| Remote Policy | — |
| Country | United States |
| Region | — |
| City | — |
| Salary Raw | Description Role Summary: We are seeking a Generative AI Engineer to build, optimize, and scale production-ready AI applications. You will design complex multi-agent systems, implement advanced RAG pipelines, and manage the deployment of both frontier and local LLMs. The ideal candidate blends deep machine learning expertise with modern software engineering practices. Technical Stack: LLMs: Gemini, OpenAI, Claude, Llama, and Local Model deployment. Frameworks: LangChain, LlamaIndex, and Hugging Face. Orchestration: LangGraph and Multi-Agent Systems (MAS). Development: Python, FastAPI, and Asynchronous Programming. RAG & Data: PostgreSQL, Vector Databases, and Advanced Retrieval strategies. ML/DL: PyTorch, TensorFlow, and Model Fine-tuning. Deployment: Docker, Production API management, and LLM monitoring. Tools: Prompt Engineering, Workflow Design, and GenAI Optimization. Key Responsibilities: Develop and orchestrate sophisticated AI workflows using LangGraph and multi-agent architectures. Build and maintain Advanced RAG systems utilizing LlamaIndex and vector databases for high-accuracy retrieval. Integrate and swap diverse LLMs (commercial and open-source) based on performance and cost requirements. Design and deploy high-performance, scalable backend services using FastAPI and Async Python. Fine-tune large language models (LLMs) using PyTorch/TensorFlow to improve domain-specific performance. Optimize GenAI workflows for latency, cost, and reliability using advanced prompt engineering and monitoring tools. Containerize and deploy AI services via Docker to production environments. Required Qualifications: 7+ years of experience ; Hands-on experience building and deploying GenAI applications in a production setting. Strong proficiency in Python and the modern AI library ecosystem (LangChain, LlamaIndex, etc.). Experience with vector search, embedding models, and advanced data retrieval patterns. Knowledge of model fine-tuning techniques and local LLM quantization/hosting. Familiarity with production-grade monitoring, API security, and CI/CD for ML. Compensation, Benefits and Duration Minimum Compensation: USD 53,000 Maximum Compensation: USD 188,000 Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role. Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees. This position is not available for independent contractors No applications will be considered if received more than 120 days after the date of this post |
| Salary Min | — |
| Salary Max | — |
| Salary Currency | — |
| Salary Period | — |
| Source URL | https://fa-ertb-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_2/job/25962 |
| Apply URL | https://fa-ertb-saasfaprod1.fa.ocs.oraclecloud.com/hcmUI/CandidateExperience/en/sites/CX_2/job/25962 |
| First Seen At | 2026-05-31 18:10:54Z |
| Last Seen At | 2026-06-06 11:42:19Z |
| Last Checked At | 2026-06-06 11:42:19Z |
| Last Changed At | 2026-05-31 18:10:54Z |
| Inactive At | — |
| Source Posted At | 2026-05-13 19:28:34Z |
| Source Updated At | — |
| Raw Payload Uri | s3://job-postings-prod-raw-590183727216/raw/provider=oracle_hcm/board=fa-ertb-saasfaprod1.fa.ocs.oraclecloud.com|CX_2/date=2026-06-06/2026-06-06T11-39-58-531Z-e320342a62e45378bca2a748525babe6f39f646f8831572ba82a69bb753d37a3.json |
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