Level AI is a Series C startup founded in 2019 and headquartered in Mountain View, California.
The company focuses on revolutionizing customer engagement by transforming contact centers into strategic assets.
Level AI's AI-native platform utilizes advanced technologies such as Large Language Models to extract deep insights from customer interactions.
The platform provides actionable intelligence that empowers organizations to enhance customer experience and drive growth.
As a Senior Machine Learning Engineer, you will work with cutting-edge technologies and play a high-impact role in shaping the future of AI-driven enterprise applications.
You will collaborate with professionals who have experience at top technology companies like Amazon, Facebook, and Google.
The role offers opportunities for fun, learning, and growth within the company.
Requirements:
Candidates must have a B.E/B.Tech/M.E/M.Tech/PhD from tier 1 engineering institutes with relevant work experience at a top technology company in computer science or mathematics-related fields.
A minimum of 3 years of experience in AI/ML is required.
Strong coding skills in Python and familiarity with libraries such as LangChain or Transformers are essential.
An interest in Large Language Models (LLMs), agents, and the evolving open-source AI ecosystem is necessary.
Candidates should demonstrate eagerness to learn, experiment, and grow in a fast-paced environment.
Benefits:
The position offers the opportunity to assist in building LLM-powered agents for internal tools and customer-facing products.
You will support prompt engineering, retrieval-augmented generation (RAG), and tool integrations.
The role includes collaboration on experiments with both open-source and commercial LLMs (e.g., GPT, Claude, Mistral).
You will help implement and evaluate reasoning, planning, and memory modules for agents.
The position involves working closely with senior engineers to deploy and monitor AI features in production.
Bonus points for experience with open-source LLMs, basic understanding of vector search, RAG, and prompt engineering concepts, contributions to AI side projects or GitHub repositories, and exposure to vector databases or retrieval pipelines (e.g., FAISS, Pinecone).
