Job
Description
Role Overview: As an experienced AI Engineer with 4-5 years of hands-on experience in designing and implementing AI solutions, you will have the opportunity to work on cutting-edge AI applications using Large Language Models (LLMs) like GPT, LLaMA, or custom fine-tuned models. Your role will involve collaborating closely with Product, Data Science, and Engineering teams to define, develop, and optimize AI/ML models for real-world business applications. Key Responsibilities: - Research, design, and develop AI/ML solutions for real-world business applications, with Retrieval-Augmented Generation (RAG) being a must. - Collaborate with Product & Data Science teams to define core AI/ML platform features. - Analyze business requirements and identify pre-trained models that align with use cases. - Work with multi-agent AI frameworks like LangChain, LangGraph, and LlamaIndex. - Train and fine-tune LLMs (GPT, LLaMA, Gemini, etc.) for domain-specific tasks. - Implement Retrieval-Augmented Generation (RAG) workflows and optimize LLM inference. - Develop NLP-based GenAI applications, including chatbots, document automation, and AI agents. - Preprocess, clean, and analyze large datasets to train and improve AI models. - Optimize LLM inference speed, memory efficiency, and resource utilization. - Deploy AI models in cloud environments (AWS, Azure, GCP) or on-premises infrastructure. - Develop APIs, pipelines, and frameworks for integrating AI solutions into products. - Conduct performance evaluations and fine-tune models for accuracy, latency, and scalability. - Stay updated with advancements in AI, ML, and GenAI technologies. Qualifications Required: - Strong experience in developing & deploying AI/ML models. - Expertise in LLM pretraining, fine-tuning, and optimization. - Hands-on experience in NLP, Transformers, OpenCV, YOLO, R-CNN. - Experience with LangChain, LangGraph, LlamaIndex. - Proficiency in TensorFlow, PyTorch, Keras. - Strong knowledge of AWS, Azure, or GCP for AI deployment. - Experience in LLM inference optimization for speed & memory efficiency. - Proficiency in Python and experience in API development. - Knowledge of Regression, Classification, Clustering, SVMs, Decision Trees, Neural Networks. - Strong skills in unit testing, debugging, and model evaluation. - Hands-on experience with Vector Databases (FAISS, ChromaDB, Weaviate, Pinecone). Additional Details: Good to Have: - Experience with multi-modal AI (text, image, video, speech processing). - Familiarity with containerization (Docker, Kubernetes) and model serving (FastAPI, Flask, Triton). Role Overview: As an experienced AI Engineer with 4-5 years of hands-on experience in designing and implementing AI solutions, you will have the opportunity to work on cutting-edge AI applications using Large Language Models (LLMs) like GPT, LLaMA, or custom fine-tuned models. Your role will involve collaborating closely with Product, Data Science, and Engineering teams to define, develop, and optimize AI/ML models for real-world business applications. Key Responsibilities: - Research, design, and develop AI/ML solutions for real-world business applications, with Retrieval-Augmented Generation (RAG) being a must. - Collaborate with Product & Data Science teams to define core AI/ML platform features. - Analyze business requirements and identify pre-trained models that align with use cases. - Work with multi-agent AI frameworks like LangChain, LangGraph, and LlamaIndex. - Train and fine-tune LLMs (GPT, LLaMA, Gemini, etc.) for domain-specific tasks. - Implement Retrieval-Augmented Generation (RAG) workflows and optimize LLM inference. - Develop NLP-based GenAI applications, including chatbots, document automation, and AI agents. - Preprocess, clean, and analyze large datasets to train and improve AI models. - Optimize LLM inference speed, memory efficiency, and resource utilization. - Deploy AI models in cloud environments (AWS, Azure, GCP) or on-premises infrastructure. - Develop APIs, pipelines, and frameworks for integrating AI solutions into products. - Conduct performance evaluations and fine-tune models for accuracy, latency, and scalability. - Stay updated with advancements in AI, ML, and GenAI technologies. Qualifications Required: - Strong experience in developing & deploying AI/ML models. - Expertise in LLM pretraining, fine-tuning, and optimization. - Hands-on experience in NLP, Transformers, OpenCV, YOLO, R-CNN. - Experience with LangChain, LangGraph, LlamaIndex. - Proficiency in TensorFlow, PyTorch, Keras. - Strong knowledge of AWS, Azure, or GCP for AI deployment. - Experience in LLM inference optimization for speed & memory efficiency. - Proficiency in Python and experience in API development. - Knowledge of Regression, Classification, Clustering, SVMs, Decision Trees, Neural Networks. - Strong skills in unit testing, debugging, and model evaluation. - Hands-on experience with Vector Databases (FAISS, ChromaDB, Weaviate, Pinecone). Additional
