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Remote, mid-level LLM specialization, metro location and popular GenAI demand create high applicant competition.
LLM/GenAI skills are highly transferable across industries; EV domain knowledge is only a bonus.
Explicit 4–6 years requirement and mandatory LLM frameworks, vector DBs, and FastAPI make shortlisting highly strict.
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Design, develop, and maintain LLM-powered backend services using Python and FastAPI for conversational AI interacting with fleet data.
Implement and optimize retrieval-augmented generation (RAG) pipelines using frameworks like LangChain, LlamaIndex, or Haystack to manage data retrieval and query routing.
Ensure secure integration and efficient data flow between backend and OXRED APIs; develop evaluation pipelines to benchmark retrieval and response performance.
4–6 years hands-on experience in Machine Learning with proven expertise building LLM or Generative AI applications using LangChain, LlamaIndex, or similar frameworks.
Proficiency in Python and FastAPI; experience with retrieval pipelines, SQL or CrateDB/PostgreSQL, and vector databases (FAISS, Chroma, Pinecone).
Ability to design prompting strategies and retrieval pipelines for both structured and unstructured data.
Work Experience Required: 4–6 years in relevant ML and LLM development roles.
Experienced in building and deploying production-grade LLM applications with focus on retrieval-augmented generation and pipeline optimization.
Skilled in backend service development and integrating AI models within complex data ecosystems, specifically in fleet or IoT domains.
Familiar with EV analytics or fleet management data and motivated to work on scalable AI solutions with measurable impact on vehicle fleet performance.