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Remote role and general AI title increase applicants, but LLM specialization and smaller company moderate competition.
LLM and production ML skills are transferable across industries but require specific model and infra expertise.
Multiple mandatory technical requirements (LLMs, RAG, vector DBs, cloud, Docker/K8s) make shortlisting strict.
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Design, develop, and deploy AI applications powered by Large Language Models (LLMs), including building Retrieval-Augmented Generation (RAG) pipelines using vector databases.
Integrate and fine-tune LLM APIs and open-source models while optimizing model performance, inference latency, and operational costs.
Build REST APIs and microservices, implement AI guardrails including content moderation and security, and collaborate with cross-functional teams to deliver production-ready scalable AI solutions.
Strong proficiency in Python programming.
Hands-on experience with Large Language Models, Generative AI, and AI frameworks such as LangChain, LlamaIndex, CrewAI, or AutoGen.
Experience with RAG architectures, vector databases (e.g., Pinecone, FAISS), embedding models, and semantic search.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in working with multiple LLM platforms and APIs including OpenAI, Anthropic, Google Gemini, Azure OpenAI, or open-source LLMs.
Skilled in backend AI development with REST API frameworks like FastAPI or Flask and knowledgeable in cloud platforms (AWS, Azure, or GCP).
Familiar with DevOps tools such as Docker, Kubernetes, CI/CD pipelines, and best practices in software development and AI model monitoring.