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Specialized LLM/RAG skills but non-metro location and modest employer brand moderate competition.
Strong ML/NLP focus is transferable across industries but requires specialized experience.
Explicit 6–8 years plus mandatory LLM, RAG, vector DB and MLOps skills make filters stringent.
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Design and maintain scalable data pipelines for both structured and unstructured data.
Develop, deploy, and manage AI/ML models focusing on NLP, transformer-based architectures, LLMs, and Generative AI solutions including retrieval-augmented generation (RAG) pipelines.
Implement knowledge graphs and semantic search, and manage MLOps workflows including CI/CD and monitoring.
6+ years full-time professional experience in relevant roles.
Proficient in Python programming and experienced in NLP, ML, and Data Science domains.
Strong expertise in LLMs, Transformers (e.g., BERT, GPT), Generative AI, RAG, embeddings, and vector databases (FAISS/Pinecone).
Hands-on experience with knowledge graphs (Neo4j, RDF), TensorFlow/PyTorch, Hugging Face, LangChain, and cloud platforms (AWS/Azure/GCP) including MLOps tools like Docker and CI/CD.
Experienced engineer able to design end-to-end scalable AI/ML pipelines from data ingestion to deployment and monitoring.
Technical depth in transformer architectures, LLMs, and advanced Generative AI implementations, with practical exposure to emerging AI trends.
Comfortable working in cross-functional environments translating business requirements into production-grade AI solutions with MLOps best practices.