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Remote role, metro location, and broad non-niche skillset elevate applicant competition.
LLM engineering skills are transferable across industries but require specialized model and infrastructure experience.
Many mandatory LLM frameworks, vector DBs, API, and infra requirements create strict technical filters.
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Design, develop, and deploy AI applications and agents powered by Large Language Models (LLMs), including building RAG pipelines.
Integrate and optimize LLM APIs and open-source models, develop AI workflows with frameworks like LangChain and LlamaIndex, and ensure AI guardrails and security.
Build REST APIs and microservices, optimize inference latency and costs, and collaborate with product and engineering teams to deliver production-ready AI solutions.
Strong proficiency in Python programming.
Hands-on experience with Large Language Models (LLMs), Generative AI, and associated frameworks such as LangChain, LlamaIndex, CrewAI, or AutoGen.
Experience with RAG architectures and vector databases like Pinecone, Weaviate, Chroma, FAISS, or Milvus.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in deploying scalable AI systems with cross-functional collaboration across product, engineering, and data teams.
Technically adept in deploying and fine-tuning LLMs with a focus on optimizing AI workflows and performance.
Comfortable working with cloud platforms (AWS, Azure, GCP), containerization (Docker, Kubernetes), and advanced AI tooling for production environments.