Gen AI + ML Engineer

Job Overview\nWe are looking for a skilled Gen AI + ML Engineer with strong expertise in Generative AI, Machine Learning, and Deep Learning. The ideal candidate will have hands-on experience building and deploying ML models, working with Large Language Models (LLMs), and developing AI-driven solutions using modern ML frameworks.\n\nApplicants should have hands-on experience in Gen AI + Python and knowledge of Large Language Models (LLMs), RAG pipelines, embeddings, and prompt engineering. Practical experience with AI-driven and GenAI applications is preferred.\nKey Responsibilities\nDesign, develop, and deploy Machine Learning and Deep Learning models for production-grade applications.\nBuild and fine-tune Large Language Models (LLMs) for domain-specific use cases.\nDevelop and optimize RAG (Retrieval-Augmented Generation) pipelines, embeddings, and vector databases.\nImplement prompt engineering strategies to improve LLM output quality and accuracy.\nCollaborate with cross-functional teams to integrate Gen AI capabilities into existing products and platforms.\nConduct model evaluation, A/B testing, and performance benchmarking for ML/AI solutions.\nStay updated with the latest advancements in Gen AI, NLP, and ML research and apply them to real-world problems.\nDevelop and maintain ML pipelines for data preprocessing, feature engineering, model training, and inference.\n'05; Must-Have Skills\nStrong hands-on experience in Machine Learning, Deep Learning, and Generative AI.\nProficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers.\nExperience with Large Language Models (LLMs) — GPT, LLaMA, Mistral, or similar.\nWorking knowledge of RAG pipelines, vector databases (Pinecone, Weaviate, FAISS), and embeddings.\nSolid understanding of prompt engineering, fine-tuning, and RLHF techniques.\nExperience with NLP tasks — text classification, NER, summarization, question answering, and sentiment analysis.\nFamiliarity with cloud platforms (AWS, Azure, or GCP) for ML model deployment.\nGood-to-Have Skills\nExperience with MLOps tools (MLflow, Kubeflow, or similar) for model lifecycle management.\nKnowledge of LangChain, LlamaIndex, or similar orchestration frameworks.\nExposure to computer vision or multimodal AI models.\nExperience with containerization (Docker, Kubernetes) for ML workloads.