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Popular ML title, metro location, and broad AI skill requirements increase applicant density.
ML/AI skills transfer moderately well, but LLM/agent specialization increases domain specificity.
Explicit 0–2 years plus many required ML/LLM skills creates moderate filtering.
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Contribute to production-grade AI systems focusing on AI pipelines, APIs, and agent systems with guidance from senior ML engineers.
Implement and improve components related to LLMs, RAG architectures, AI agents, and backend engineering including Flask APIs and Python coding.
Engage in code reviews and maintain production-quality codebase while utilizing AI-assisted tools for code navigation and development acceleration.
0 to 2 years of work experience in machine learning or related fields.
Strong programming skills in Python, including API development (Flask) and Git expertise.
Clear understanding of LLM fundamentals including tokenization, prompting techniques, and recent model developments.
Working knowledge of RAG architecture, AI agents, neural networks basics, and familiarity with computer vision/document AI concepts (OCR, image processing).
Comfortable working within engineering teams with moderate supervision, able to independently navigate complex codebases using AI tools.
Capable of contributing to system design and architecture discussions, focusing on practical and scalable AI solutions.
Familiar with modern AI toolchains and frameworks (e.g., LangChain, Hugging Face Transformers) and up to date with recent advancements in LLMs and AI engineering.