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Niche LLM skills reduce applicant pool, but ML intern titles attract many applicants.
Requires specialized LLM, RAG, and Python backend skills, limiting cross-industry transferability.
Numerous non-negotiable technical requirements and demonstrated practical ML engineering skills required.
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Contribute to the development and maintenance of LLM-powered document intelligence pipelines, RAG systems, AI agent workflows, and APIs for client-facing AI deployments.
Execute tasks independently with minimal guidance, converting vague requirements into working code while ensuring high code quality, correctness, and documentation.
Use AI tools to accelerate development but maintain deep technical understanding to debug and make architectural decisions without over-reliance on AI assistance.
Strong understanding of LLM fundamentals, prompting techniques, and ability to run inference on local LLMs using tools like Ollama or Hugging Face Transformers.
Proficient in Python backend engineering including API development (Flask), structured output handling (JSON schema, Pydantic), and version control (Git).
Working knowledge of RAG architecture, AI agents, transformer basics, and codebase navigation with AI-assisted tools.
Work Experience Required: Not explicitly mentioned in the JD
Comfortable operating embedded within an engineering team with exposure to production-level AI systems and code reviews from day one.
Able to independently drive feature development and improvements in LLM and AI-based projects with strong communication and collaboration skills.
Has baseline knowledge of computer vision and document AI concepts, and familiarity with AI orchestration frameworks like LangChain or LlamaIndex.