Role overview

Senior AI Engineer (LLM)

Requirements and responsibilities

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About us:

  • Expert-level Python skills with a deep understanding of asynchronous programming and backend architecture.
  • Proven experience building and deploying production-level applications using Large Language Models (LLMs).
  • Strong experience with SQL and NoSQL databases, specifically optimized for high-dimensional vector search.
  • Familiarity with deploying AI models in cloud environments (AWS, GCP, or Azure) and managing CI/CD for AI services.
  • A "first-principles" approach to solving the unique challenges of non-deterministic AI outputs
  • Experience with fine-tuning open-source models using techniques like LoRA or QLoRA.
  • Knowledge of agentic frameworks and autonomous AI agents.
  • A background in traditional NLP (Spacy, NLTK) or classic Machine Learning.

About us:

  • Architect and implement complex AI workflows using frameworks like LangChain, LlamaIndex, or Haystack.
  • Design and optimize Retrieval-Augmented Generation (RAG) pipelines, including vector database management (Pinecone, Weaviate, or Milvus) and advanced indexing strategies.
  • Evaluate and implement strategies for model selection (OpenAI, Anthropic, or Open Source like Llama 3) based on latency, cost, and performance requirements.
  • Develop high-performance Python backends (FastAPI or Flask) to serve AI features to our global user base.
  • Build robust evaluation frameworks to measure LLM accuracy, reduce hallucinations, and monitor production performance using tools like LangSmith or Arize Phoenix.
  • Work closely with product and engineering teams to identify high-impact AI opportunities and navigate the technical trade-offs of generative AI.
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Browse stack
FocuspythonRole area
Seniority signalLeadCandidate level
StackLLM, AI, AWSPrimary skills
LocationWorldwideEligibility

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