Data Scientist and AI Engineer

As a Data Scientist / AI Engineer at Fulcrum Digital, you will play a key role in designing, training, and evaluating ML models for various tasks such as classification, anomaly detection, forecasting, and natural language understanding. You will have the opportunity to work with traditional ML algorithms as well as advanced deep learning and Generative AI systems. Your responsibilities will include building and fine-tuning deep learning models, deploying Generative AI solutions, performing model optimization techniques, and collaborating with cross-functional teams to productionize models. Your expertise in Python, ML/DL libraries, model quantization, and deployment on constrained environments will be essential for success in this role. **Key Responsibilities:** - Design, train, and evaluate ML models for classification, anomaly detection, forecasting, and natural language understanding. - Build and fine-tune deep learning models including RNNs, GRUs, LSTMs, and Transformer architectures. - Develop and deploy Generative AI solutions such as RAG pipelines for document search, Q&A, and summarization. - Perform model optimization techniques like quantization for improving latency and reducing memory/compute overhead. - Collaborate with cross-functional teams to productionize models using CI/CD pipelines. - Write clean, reproducible code and maintain proper versioning and documentation of experiments. **Qualifications Required:** - 45 years of hands-on experience in machine learning or data science roles. - Proficiency in Python and ML/DL libraries like scikit-learn, pandas, PyTorch, TensorFlow. - Strong knowledge of traditional ML and deep learning, especially for sequence and NLP tasks. - Experience with Transformer models, open-source LLMs, and Generative AI tools. - Familiarity with model quantization and deployment on constrained environments. - Proficiency in evaluating models using statistical and business metrics. - Experience with model deployment, monitoring, and performance tuning in production environments. - Familiarity with Docker, MLflow, and CI/CD practices. In addition, preferred qualifications include experience fine-tuning LLMs on domain-specific datasets, exposure to MLOps platforms, familiarity with distributed data processing and orchestration tools, and contributions to research papers or open-source projects in ML/NLP/GenAI. This role is suitable for individuals at the Mid Level of their career with 45 years of relevant experience in machine learning or data science roles. As a Data Scientist / AI Engineer at Fulcrum Digital, you will play a key role in designing, training, and evaluating ML models for various tasks such as classification, anomaly detection, forecasting, and natural language understanding. You will have the opportunity to work with traditional ML algorithms as well as advanced deep learning and Generative AI systems. Your responsibilities will include building and fine-tuning deep learning models, deploying Generative AI solutions, performing model optimization techniques, and collaborating with cross-functional teams to productionize models. Your expertise in Python, ML/DL libraries, model quantization, and deployment on constrained environments will be essential for success in this role. **Key Responsibilities:** - Design, train, and evaluate ML models for classification, anomaly detection, forecasting, and natural language understanding. - Build and fine-tune deep learning models including RNNs, GRUs, LSTMs, and Transformer architectures. - Develop and deploy Generative AI solutions such as RAG pipelines for document search, Q&A, and summarization. - Perform model optimization techniques like quantization for improving latency and reducing memory/compute overhead. - Collaborate with cross-functional teams to productionize models using CI/CD pipelines. - Write clean, reproducible code and maintain proper versioning and documentation of experiments. **Qualifications Required:** - 45 years of hands-on experience in machine learning or data science roles. - Proficiency in Python and ML/DL libraries like scikit-learn, pandas, PyTorch, TensorFlow. - Strong knowledge of traditional ML and deep learning, especially for sequence and NLP tasks. - Experience with Transformer models, open-source LLMs, and Generative AI tools. - Familiarity with model quantization and deployment on constrained environments. - Proficiency in evaluating models using statistical and business metrics. - Experience with model deployment, monitoring, and performance tuning in production environments. - Familiarity with Docker, MLflow, and CI/CD practices. In addition, preferred qualifications include experience fine-tuning LLMs on domain-specific datasets, exposure to MLOps platforms, familiarity with distributed data processing and orchestration tools, and contributions to research papers or open-source projects in ML/NLP/GenAI. This role is suitable for individuals at the Mid Level of thei