AI Engineer 3

Rippling • IT Service & Asset Management • HR & Payroll Software • Bachelor's Degree • Python • SQL • 2 months ago
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Salary
≈ $24k – $57k per year (Estimated)
Location
In office (Bengaluru)
Seniority
Middle · 5+ years exp

First seen by Alion on Aug 31, 2026.

Overview
Company
Impact
Profile match

Rippling

Rippling is an enterprise workforce management and workforce OS platform headquartered in San Francisco, California. Founded in 2016 by Parker Conrad and Prasanna Sankar, the company provides a unified software system that manages human resources (HR), information technology (IT), and corporate finance operations. Operating on a B2B SaaS subscription model, Rippling sits on top of a centralized employee data store. Its integrated product suite covers global payroll, employee benefits administration, applicant tracking, device management, corporate expense management, corporate cards, and bill pay.
rippling.com • HQ: San Francisco, United States • Procurement & Spend Management • IT Service & Asset Management • HR & Payroll Software • 501-1000 employees • Est. 2016
Tech stack of Rippling
Backed by Sequoia Capital, Y Combinator, Kleiner Perkins

Requirements:

  • Bachelor's or master's degree in computer science, artificial intelligence, machine learning, or a related field.
  • 5-8+ years of software engineering or machine learning experience, including at least 3 years focused on NLP and LLMs.
  • Strong programming skills in Python.
  • Experience with PyTorch and/or TensorFlow.
  • Hands-on experience with Hugging Face Transformers, PEFT, LoRA, QLoRA, and model fine-tuning techniques.
  • Strong understanding of transformer architectures, attention mechanisms, embeddings, and tokenization.
  • Experience working with open-source LLMs such as Llama, Mistral, Qwen, Gemma, or similar models.
  • Experience building RAG applications using vector databases such as Pinecone, Weaviate, Milvus, Chroma, or FAISS.
  • Familiarity with LangChain, LangGraph, LlamaIndex, or similar AI orchestration frameworks.
  • Experience with cloud platforms (AWS, Azure, or Google Cloud Platform).
  • Knowledge of Docker, Kubernetes, CI/CD pipelines, and ML deployment practices.
  • Experience with SQL, NoSQL databases, and data processing frameworks.
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