Senior Data Scientist, SageMaker, Bedrock

• Own Data Science projects end-to-end, from technical discovery through data analysis, solution design, experimentation, implementation, deployment, and production validation
• Evaluate and select technical approaches including prompt engineering, RAG, GenAI, agentic workflows, fine-tuning, smaller language models, classical ML, Computer Vision, recommendation systems, and custom model training
• Analyze and prepare customer datasets, identify data quality issues, create representative validation and golden datasets, and assess data suitability for modeling
• Train, fine-tune, optimize, evaluate, and deploy ML models using Python, PyTorch/TensorFlow, SageMaker, and modern ML tooling
• Build and evaluate Generative AI solutions using Amazon Bedrock, RAG, prompt engineering, model selection, agentic workflows, and AWS-native AI services
• Use SageMaker Studio, training jobs, endpoints, pipelines, model registry, batch inference, monitoring, and other production ML capabilities
• Design production-ready architectures balancing quality, latency, cost, scalability, maintainability, observability, and operational complexity
• Participate in customer technical discovery, workshops, architecture discussions, and delivery conversations with founders, CTOs, engineering teams, and technical stakeholders
• Challenge technical assumptions, explain trade-offs and costs, and recommend simpler or more effective approaches
• Independently own projects with minimal supervision and mentor less experienced Data Scientists and engineers when needed
• Collaborate with AI Engineers, MLOps, Data Engineering, DevOps, and Solution Architecture teams on cross-domain customer solutions

• 5+ years of experience in Data Science, Machine Learning, Applied Science, or a closely related role
• Strong classical Machine Learning fundamentals and hands-on experience building production ML solutions
• Practical experience with data preparation, validation, feature engineering, dataset construction, and model evaluation
• Strong Python skills and hands-on experience with PyTorch and/or TensorFlow
• Strong practical experience with AWS SageMaker beyond notebook-level usage, including model training, deployment, inference, pipelines, or production operations
• Hands-on experience with Amazon Bedrock and modern Generative AI approaches
• Practical experience with model fine-tuning and understanding when fine-tuning is preferable to prompting, RAG, or other approaches
• Experience in at least one ML domain such as NLP, Computer Vision, recommendation systems, forecasting, structured ML, or multimodal ML
• Understanding of RAG, embeddings, prompt engineering, foundation models, and agentic workflows
• Strong understanding of MLOps and production ML practices, including model deployment, monitoring, reproducibility, lifecycle management, and CI/CD
• Experience designing and owning solutions independently rather than working only from predefined technical specifications
• Strong customer-facing communication skills and ability to explain technical trade-offs clearly
• Ability to work with ambiguity, messy real-world data, and changing customer requirements
• Strong technical judgment and a pragmatic approach to balancing model quality with delivery speed, cost, and business value
• Experience with AgentCore, Bedrock Agents, LangGraph, Strands Agents, or other agentic frameworks is a plus
• Experience with Small Language Models or domain-specific model adaptation is a plus
• Experience with recommendation systems, audio ML, signal processing, or multimodal systems is a plus
• Experience in technical consulting, pre-sales, or customer discovery is a plus
• AWS Machine Learning certifications are a plus
• Master’s degree or PhD in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field is a plus
• CV must be submitted in English

• Sponsored AWS certifications
• Access to internal “Expert-led” knowledge sharing
• Early access to beta AWS features
• Clear progression and defined career paths from engineering to leadership
• Collaborative, supportive, and transparent culture
• Mentorship and collective success
• Opportunity to work with cutting-edge AWS, AI, DevOps, FinOps, and Kubernetes technologies
• Opportunity to work on large-scale, meaningful projects with startups and scaleups
• Global reach with stability of a mature, profitable company and the agility of a startup
• Equal-opportunity workplace