AI/ML Engineer - LLM/RAG

Job Description : - Minimum of 3+ years of experience in AI-based application development. - Experience fine-tuning Large Language Models (LLMs) or working with domain-specific AI models. - Experience building production-grade AI applications using RAG, AI Agents, or multi-agent systems. - Familiarity with Vector Search, Knowledge Graphs, and AI observability/evaluation frameworks. - Experience deploying AI/ML solutions using Docker, Kubernetes, and cloud-native services. - Contributions to open-source AI projects, research publications, or participation in AI communities is a plus. - Proficiency in Python (libraries like NumPy, Pandas, Scikit-learn, and PyTorch/TensorFlow). - Strong understanding of Machine Learning (ML) and Deep Learning (DL) algorithms. - Hands-on experience with Computer Vision techniques (image classification, object detection, segmentation). - Practical experience with Generative AI and Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Claude, Gemini, or similar models. - Experience building RAG (Retrieval-Augmented Generation) pipelines for enterprise applications. - Hands-on experience building AI Agents and agentic workflows using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, or similar orchestration frameworks. - Familiarity with No-Code / Low-Code AI automation platforms such as n8n, Dify, Flowise, or LangFlow. - Hands-on experience with Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS, or Qdrant). - Experience with classification tasks and building predictive models. - Proficiency with deep learning frameworks : PyTorch and/or TensorFlow / Keras. - Exposure to NLP techniques or libraries (NLTK, Hugging Face, spaCy). - Exposure to cloud platforms such as AWS, Azure, or GCP. - Familiarity with MLOps practices and tools (Docker, MLflow, Git, CI/CD). - Strong analytical and problem-solving abilities with a passion for solving real-world business challenges. - Ability to translate business requirements into scalable AI and machine learning solutions. - Excellent communication and collaboration skills for working with cross-functional teams and clients. - Self-motivated learner with enthusiasm for exploring emerging AI technologies and industry trends. - Ability to work independently while effectively managing priorities in a fast-paced environment. - Strong ownership mindset with attention to quality, performance, and continuous improvement. Job Description : - Minimum of 3+ years of experience in AI-based application development. - Experience fine-tuning Large Language Models (LLMs) or working with domain-specific AI models. - Experience building production-grade AI applications using RAG, AI Agents, or multi-agent systems. - Familiarity with Vector Search, Knowledge Graphs, and AI observability/evaluation frameworks. - Experience deploying AI/ML solutions using Docker, Kubernetes, and cloud-native services. - Contributions to open-source AI projects, research publications, or participation in AI communities is a plus. - Proficiency in Python (libraries like NumPy, Pandas, Scikit-learn, and PyTorch/TensorFlow). - Strong understanding of Machine Learning (ML) and Deep Learning (DL) algorithms. - Hands-on experience with Computer Vision techniques (image classification, object detection, segmentation). - Practical experience with Generative AI and Large Language Models (LLMs) such as GPT, LLaMA, Mistral, Claude, Gemini, or similar models. - Experience building RAG (Retrieval-Augmented Generation) pipelines for enterprise applications. - Hands-on experience building AI Agents and agentic workflows using frameworks such as LangChain, LangGraph, CrewAI, LlamaIndex, or similar orchestration frameworks. - Familiarity with No-Code / Low-Code AI automation platforms such as n8n, Dify, Flowise, or LangFlow. - Hands-on experience with Vector Databases (Pinecone, Weaviate, ChromaDB, FAISS, or Qdrant). - Experience with classification tasks and building predictive models. - Proficiency with deep learning frameworks : PyTorch and/or TensorFlow / Keras. - Exposure to NLP techniques or libraries (NLTK, Hugging Face, spaCy). - Exposure to cloud platforms such as AWS, Azure, or GCP. - Familiarity with MLOps practices and tools (Docker, MLflow, Git, CI/CD). - Strong analytical and problem-solving abilities with a passion for solving real-world business challenges. - Ability to translate business requirements into scalable AI and machine learning solutions. - Excellent communication and collaboration skills for working with cross-functional teams and clients. - Self-motivated learner with enthusiasm for exploring emerging AI technologies and industry trends. - Ability to work independently while effectively managing priorities in a fast-paced environment. - Strong ownership mindset with attention to quality, performance, and continuous improvement.