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# AI Engineer

- **Company:** NTT DATA, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Big Data, Cloud Computing, Computer Programming, Continuous Integration, Machine Learning, Open Source Technology, Recommender Systems, Tensorflow, Software Safety, Software Engineering, Systems Architecture, Large Language Models, Prompt Engineering, Apache Spark, Deep Learning, Generative AI, Fastapi, Containerization, Scikit Learn, Kubernetes, Xgboost, Machine Learning Operations, Docker
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f9746dd91b9c0b99

## About the Role

* 8-10 years in Machine Learning or Software Engineering, with 4+ years deploying Deep Learning models to production.
 * 8+ years experience in Programming & Frameworks: Expert in Python; extensive hands-on experience with PyTorch (preferred) or TensorFlow.
 * 4+ years experience in LLM Ecosystem: Deep knowledge of orchestration (LangChain, LlamaIndex), vector databases (Pinecone, Weaviate), and inference optimization (vLLM, quantization).
 * 6+ years experience in Traditional ML & Data: Strong grasp of statistical modeling (scikit-learn, XGBoost) and large-scale data processing (Apache Spark, Ray).
 * 6+ years experience in MLOps & Cloud: Proficiency with Cloud platforms (AWS/GCP/Azure), containerization (Docker, Kubernetes), model tracking (MLflow), and API deployment (FastAPI).

Education: Master's or Ph.D. in CS, AI, Math, or equivalent practical experience

Preferred: Open-source AI contributions, experience with multi-modal models, or a background in AI guardrails. "

## Description

We are seeking an experienced technical leader to architect and scale our AI systems. You will bridge traditional machine learning with state-of-the-art Large Language Models (LLMs), acting as the technical anchor to drive our AI strategy from research to production., * System Architecture: Design, build, and scale secure, cost-effective ML pipelines and Generative AI applications.

 * LLM Integration: Lead development using advanced RAG architectures, prompt engineering, and fine-tuning (PEFT/LoRA) on models like Llama 3, Gemini, and OpenAI.
 * Core Machine Learning: Train and optimize predictive models, classifiers, and recommendation systems using deep learning and classical ML.
 * MLOps & Leadership: Oversee model deployment (CI/CD for ML), monitor for drift, ensure AI safety, and mentor junior engineers.


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