Generative AI & Machine Learning Engineer (Kolkata)

Job Overview We 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. Applicants 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. Key Responsibilities - Design, develop, and deploy Machine Learning and Deep Learning models for production-grade applications. - Build and fine-tune Large Language Models (LLMs) for domain-specific use cases. - Develop and optimize RAG (Retrieval-Augmented Generation) pipelines, embeddings, and vector databases. - Implement prompt engineering strategies to improve LLM output quality and accuracy. - Collaborate with cross-functional teams to integrate Gen AI capabilities into existing products and platforms. - Conduct model evaluation, A/B testing, and performance benchmarking for ML/AI solutions. - Stay updated with the latest advancements in Gen AI, NLP, and ML research and apply them to real-world problems. - Develop and maintain ML pipelines for data preprocessing, feature engineering, model training, and inference. Must-Have Skills - Solid hands-on experience in Machine Learning, Deep Learning, and Generative AI. - Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. - Experience with Large Language Models (LLMs) GPT, LLaMA, Mistral, or similar. - Working knowledge of RAG pipelines, vector databases (Pinecone, Weaviate, FAISS), and embeddings. - Solid understanding of prompt engineering, fine-tuning, and RLHF techniques. - Experience with NLP tasks text classification, NER, summarization, question answering, and sentiment analysis. - Familiarity with cloud platforms (AWS, Azure, or GCP) for ML model deployment. Good-to-Have Skills - Experience with MLOps tools (MLflow, Kubeflow, or similar) for model lifecycle management. - Knowledge of LangChain, LlamaIndex, or similar orchestration frameworks. - Exposure to computer vision or multimodal AI models. - Experience with containerization (Docker, Kubernetes) for ML workloads. Job Overview We 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. Applicants 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. Key Responsibilities - Design, develop, and deploy Machine Learning and Deep Learning models for production-grade applications. - Build and fine-tune Large Language Models (LLMs) for domain-specific use cases. - Develop and optimize RAG (Retrieval-Augmented Generation) pipelines, embeddings, and vector databases. - Implement prompt engineering strategies to improve LLM output quality and accuracy. - Collaborate with cross-functional teams to integrate Gen AI capabilities into existing products and platforms. - Conduct model evaluation, A/B testing, and performance benchmarking for ML/AI solutions. - Stay updated with the latest advancements in Gen AI, NLP, and ML research and apply them to real-world problems. - Develop and maintain ML pipelines for data preprocessing, feature engineering, model training, and inference. Must-Have Skills - Solid hands-on experience in Machine Learning, Deep Learning, and Generative AI. - Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. - Experience with Large Language Models (LLMs) GPT, LLaMA, Mistral, or similar. - Working knowledge of RAG pipelines, vector databases (Pinecone, Weaviate, FAISS), and embeddings. - Solid understanding of prompt engineering, fine-tuning, and RLHF techniques. - Experience with NLP tasks text classification, NER, summarization, question answering, and sentiment analysis. - Familiarity with cloud platforms (AWS, Azure, or GCP) for ML model deployment. Good-to-Have Skills - Experience with MLOps tools (MLflow, Kubeflow, or similar) for model lifecycle management. - Knowledge of LangChain, LlamaIndex, or similar orchestration frameworks. - Exposure to computer vision or multimodal AI models. - Experience with containerization (Docker, Kubernetes) for ML workloads.