Level AI
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Level AI was founded in 2019 and is a Series C startup headquartered in Mountain View, California. Level AI revolutionizes customer engagement by transforming contact centres into strategic assets. Our AI-native platform leverages advanced technologies such as Large Language Models to extract deep insights from customer interactions. By providing actionable intelligence, Level AI empowers organisations to enhance customer experience and drive growth. Consistently updated with the latest AI innovations, Level AI stands as the most adaptive and forward-thinking solution in the industry.
Empowering contact center stakeholders with real-time insights, our tech facilitates data-driven decision-making for contact centers, enhancing service levels and agent performance. As a vital team member, your work will be cutting-edge technologies, and you will play a high-impact role in shaping the future of AI-driven enterprise applications. You will directly work with people who've worked at Amazon, Facebook, Google, and other technology companies in the world. With Level AI, you will get to have fun, learn new things, and grow along with us. Ready to redefine possibilities? Join us!
Build and deploy LLM-powered agents for internal tools and customer-facing products.
Design and improve RAG pipelines, prompt engineering workflows, and tool integrations.
Experiment with open-source/closed-source LLMs.
Implement and evaluate reasoning, planning, memory, and retrieval modules for AI agents.
Optimise ML and LLM systems for accuracy, latency, scalability, and cost.
Build production-ready AI/NLP pipelines with monitoring, evaluation benchmarks, and continual improvement loops.
Collaborate with product, engineering, and customer-facing teams to ship impactful AI features.
Strong programming skills in Python.
Interest and experience with RAG and agentic AI concepts.
Experience with PyTorch, Hugging Face Transformers, and other similar ML libraries.
Interest and experience in model training, inference optimisation, and GPUs.
Experience deploying ML services using REST APIs, Docker, Kubernetes, or cloud platforms.
Exposure to vector databases such as FAISS, Pinecone, Qdrant, or similar.
30/100
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