Sr. ML Architect

As a Technical Expertise, you are expected to have the following qualifications and responsibilities: Role Overview: You should possess knowledge and experience in using statistical and machine learning algorithms, including regression, instance-based learning, decision trees, Bayesian statistics, clustering, neural networks, deep learning, and ensemble methods. Expertise in Python with a strong proficiency in SQL and ML frameworks such as scikit-learn, TensorFlow, and PyTorch is required. Additionally, you should have completed one or more projects involving fine-tuning with LLMs. Experience in feature selection, building and optimizing classifiers, and working with backend technologies like Flask/Gunicorn is essential. You should also have extensive experience with deep learning architectures such as CNNs, RNNs, Transformers, GANs, and modern optimization techniques. Key Responsibilities: - Hands-on experience in generative AI technologies, including LLMs (GPT, BERT, T5), prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and MCP integration for context-aware AI applications - Designing and implementing AI agent architectures, multi-agent systems, and autonomous decision-making frameworks - Strong expertise in document processing, OCR, information extraction, and computer vision applications for automated validation systems - Experience with conversational AI, voice recognition technologies, and building intelligent chatbot systems - Proficiency with cloud platforms (AWS, GCP, Azure) and ML operations tools (MLflow, Kubeflow, Docker, Kubernetes) - Knowledge of data preprocessing, feature engineering, and model evaluation techniques - Proven track record of deploying ML models in production environments with demonstrated business impact - Experience with A/B testing, causal inference, and experimental design methodologies - Knowledge of workflow automation, system integration, and API development for seamless AI solution deployment Qualifications Required: - Knowledge and experience in statistical and machine learning algorithms - Expertise in Python, SQL, and ML frameworks like scikit-learn, TensorFlow, and PyTorch - Experience in fine-tuning with LLMs and feature selection - Hands-on experience with deep learning architectures and generative AI technologies - Proficiency in cloud platforms and ML operations tools - Strong knowledge of data preprocessing, feature engineering, and model evaluation techniques - Proven track record of deploying ML models in production environments - Experience with A/B testing, causal inference, and experimental design methodologies - Knowledge of workflow automation, system integration, and API development,