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Level AI

Optimizes call center data with AI

Senior Machine Learning Engineer

Natural Language Processing & LLMsUpdated on Nov 13
Full-timeRemote in IndiaNo salary listedRemote

About the Job

Requirements

  • A B.Tech, M.Tech, or PhD in computer science or mathematics-related fields from a tier-1 engineering institute, with 3+ years of industry experience in machine learning and natural language processing, or applied AI.
  • Strong programming skills in Python.
  • Interest and experience with retrieval-augmented generation and agentic AI concepts.
  • Experience with PyTorch, Hugging Face Transformers, and similar machine-learning libraries.
  • Interest and experience in model training, inference optimization, and GPUs.
  • Experience deploying machine-learning services using REST APIs, Docker, Kubernetes, or cloud platforms.
  • Exposure to vector databases such as FAISS, Pinecone, or Qdrant.

Responsibilities

  • Build and deploy large language model-powered agents for internal tools and customer-facing products.
  • Design and improve retrieval-augmented generation pipelines, prompt-engineering workflows, and tool integrations.
  • Experiment with open-source and closed-source large language models.
  • Implement and evaluate reasoning, planning, memory, and retrieval modules for AI agents.
  • Optimize machine-learning and large language model systems for accuracy, latency, scalability, and cost.
  • Build production-ready AI and natural language processing pipelines with monitoring, evaluation benchmarks, and continual-improvement loops.
  • Collaborate with product, engineering, and customer-facing teams to ship AI features.

About the Company

Level AI

Optimizes call center data with AI

Level AI provides software that improves customer experience and operational efficiency in the BPO sector by integrating human and machine intelligence. Its platform unifies call center data from multiple sources and uses intelligent automation and machine learning to automate tasks, guide decisions, and improve interactions, all delivered through a subscription model. It differentiates itself by offering end-to-end data integration across silos and blending human expertise with ML to boost sales conversions and QA productivity in call centers. The goal is to help BPO providers deliver better customer experiences while making operations more efficient and cost-effective.

Company size
201-500
Company stage
Acquired
Total funding
$74.4M
Headquarters
Mountain View, CA
Founded
2018

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