... 2 years of experience in AI/ML.- Strong programming skills in Python.- Hands-on experience with PyTorch, TensorFlow, or JAX.- Experience with LLM fine-tuning and prompt engineering.Must-Have Skills : - Experience building Agentic AI systems and reasoning workflows.- LangChain, LlamaIndex, LangGraph.- Strong understanding ...
... LlamaIndex, advanced embedding models.- GenAI Skills : Deep expertise in advanced RAG (HyDE, parent-document retrieval), prompt optimization, and parameter-efficient fine-tuning.- Qualifications : Bachelor's/Master's in CS/Data Science with 4 - 7 years in ML/AI, including 1+ years specifically working with LLMs (ref:hirist.tech) ...
... practical projects into training programs. Requirements: - Strong expertise in Python, Scikit-learn, TensorFlow, PyTorch, or Keras . - Hands-on experience with LLMs, Prompt Engineering, RAG, Vector Databases, and LLM Fine-tuning . - Experience with LangChain/LangGraph, LlamaIndex, CrewAI, AutoGen, or OpenAI Agents SDK . ...
... training, and deploying deep learning models in research or production environments.- Strong experience with deep learning and LLMs, including model training and fine-tuning.- Practical experience with data curation, model training infrastructure, and large-scale experimentation.- Experience designing and implementing ML evaluation ...
... https://jobeax.com/link/hqFuIiSQL97UDvmg We Offer:- Opportunity to work on AI/ML and emerging technology initiatives.- Exposure to real-world Machine Learning, Time Series Forecasting, and AI use cases.- Opportunity to work with modern cloud, MLOps, and data technologies.- Exposure to Generative AI, LLMs, RAG, and Agentic AI initiatives.- Collaborative ...
... Mistral, Falcon).- Familiarity with fine-tuning techniques (LoRA, QLoRA, PEFT, RLHF) and when to apply them vs. prompting/RAG.- Experience with vector databases and semantic search (Pinecone, Weaviate, Milvus, FAISS, pgvector).- Solid understanding of MLOps/LLMOps practices: model versioning, monitoring, A/B testing, CI/CD ...
... architectures, and GitHub portfolios and provide actionable feedback. - Mentor learners on Python, APIs, LLM integration, prompt engineering, embeddings, vector databases, and RAG . - Guide learners on model selection, evaluation, fine-tuning, deployment, and optimization. - Help learners understand real-world AI engineering practices ...