Senior Data Science Architect

You are a Senior Data Science Architect who will be responsible for leading the end-to-end modeling lifecycle, setting up the technical architecture for ML/GenAI, and moving business KPIs. Your role will focus on modeling excellence, MLOps, and AI solution architecture. - Own the technical vision for data-science initiatives and translate business goals into modellable problems, KPIs, and NFRs/SLOs. - Define reference architectures for classical ML, deep learning, and agentic GenAI including model registry, evaluation harness, safety/guardrails, and observability. - Lead problem decomposition, feature strategy, experiment design, error analysis, and model iteration across various domains like NLP, CV, speech, time series, recommendation, clustering/segmentation, and causal/uplift. - Architect CI/CD for models, design monitoring for accuracy, drift, data integrity, latency, cost, and safety, and orchestrate RAG pipelines, agent planning/execution, and feedback loops. - Partner with product, strategy, design, and operations to align roadmaps, provide technical mentorship to data scientists/ML engineers, and collaborate with Ops/SRE to ensure operable solutions. - Embed model governance, champion human oversight for agentic systems, and support GDPR/ISO/SOC2 requirements. - 14-20 years of experience delivering AI/ML in production, with 5+ years in an architect/tech-lead role. - Expertise in Python, PyTorch, TensorFlow, SQL, and software engineering fundamentals. - Proven record architecting scalable DS solutions on cloud platforms like AWS/Azure/GCP, hands-on experience with Docker and Kubernetes. - Proficiency in MLOps tools like MLflow, Kubeflow, model registry, pipelines, feature stores, and real-time/batch serving. - Depth across traditional ML and DL domains and strong communication skills to guide cross-functional teams. You are a Senior Data Science Architect who will be responsible for leading the end-to-end modeling lifecycle, setting up the technical architecture for ML/GenAI, and moving business KPIs. Your role will focus on modeling excellence, MLOps, and AI solution architecture. - Own the technical vision for data-science initiatives and translate business goals into modellable problems, KPIs, and NFRs/SLOs. - Define reference architectures for classical ML, deep learning, and agentic GenAI including model registry, evaluation harness, safety/guardrails, and observability. - Lead problem decomposition, feature strategy, experiment design, error analysis, and model iteration across various domains like NLP, CV, speech, time series, recommendation, clustering/segmentation, and causal/uplift. - Architect CI/CD for models, design monitoring for accuracy, drift, data integrity, latency, cost, and safety, and orchestrate RAG pipelines, agent planning/execution, and feedback loops. - Partner with product, strategy, design, and operations to align roadmaps, provide technical mentorship to data scientists/ML engineers, and collaborate with Ops/SRE to ensure operable solutions. - Embed model governance, champion human oversight for agentic systems, and support GDPR/ISO/SOC2 requirements. - 14-20 years of experience delivering AI/ML in production, with 5+ years in an architect/tech-lead role. - Expertise in Python, PyTorch, TensorFlow, SQL, and software engineering fundamentals. - Proven record architecting scalable DS solutions on cloud platforms like AWS/Azure/GCP, hands-on experience with Docker and Kubernetes. - Proficiency in MLOps tools like MLflow, Kubeflow, model registry, pipelines, feature stores, and real-time/batch serving. - Depth across traditional ML and DL domains and strong communication skills to guide cross-functional teams.