Senior Data Scientist, SageMaker, Bedrock
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Job Description
Senior Data Scientist delivering production ML and GenAI solutions on AWS for startup customers. Owning SageMaker, Bedrock, fine-tuning, and customer-facing architecture projects end-to-end.
Responsibilities:
- 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
Requirements:
- 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
Benefits:
- 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
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