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Popular ML role with broad technical demands but smaller employer reduces applicant intensity.
Requires advanced LLM, RAG, and agent engineering, limiting cross-industry transferability.
Many required ML/AI capabilities but no explicit years requirement.
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Contribute to production-grade AI systems focusing on AI pipelines, APIs, and agent systems within an engineering team.
Implement and optimize Large Language Model (LLM) related functionalities including prompting techniques and inference on local LLMs.
Engage in code reviews and maintain code quality while delivering meaningful impact from day one.
Strong programming skills in Python with experience in backend engineering and API development (Flask).
Clear understanding of LLM fundamentals, including tokenization, context windows, prompting techniques, and hallucination prevention.
Working knowledge of AI architectures such as Retrieval-Augmented Generation (RAG) and AI agent frameworks (e.g., LangChain, LlamaIndex).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in navigating and contributing to complex codebases with minimal dependency on AI tools for understanding code.
Familiarity with transformer architectures, neural network fundamentals, and AI systems design, enabling effective system architecture and scalability discussions.
Comfortable with multidisciplinary AI topics including computer vision basics, document AI, fine-tuning methods, and integrations across AI workflows and tools.