Senior Data Scientist, SageMaker, Bedrock

This is a fully remote position, open to applicants in Ukraine.

📋 Description

• Take full ownership of Data Science projects, managing them from technical discovery through data analysis, solution design, experimentation, implementation, deployment, and validation in production.

• Assess and select technical strategies, including prompt engineering, RAG, GenAI, agentic workflows, fine-tuning, smaller language models, classical ML, Computer Vision, recommendation systems, and custom model training.

• Analyze customer datasets, identify data quality challenges, create representative validation and golden datasets, and evaluate data suitability for modeling.

• Train, fine-tune, optimize, evaluate, and deploy ML models utilizing Python, PyTorch/TensorFlow, SageMaker, and other modern ML tools.

• Develop and assess Generative AI solutions using Amazon Bedrock, RAG, prompt engineering, model selection, agentic workflows, and AWS-native AI services.

• Leverage SageMaker Studio, training jobs, endpoints, pipelines, model registry, batch inference, monitoring, and additional production ML capabilities.

• Architect production-ready solutions that balance quality, latency, cost, scalability, maintainability, observability, and operational complexity.

• Engage in customer technical discovery, workshops, architecture discussions, and delivery conversations with founders, CTOs, engineering teams, and other technical stakeholders.

• Challenge technical assumptions, articulate trade-offs and costs, and suggest simpler or more effective methodologies.

• Independently manage projects with minimal oversight and mentor junior Data Scientists and engineers as necessary.

• Work collaboratively with AI Engineers, MLOps, Data Engineering, DevOps, and Solution Architecture teams to create cross-domain customer solutions.

⛳️ Requirements

• A minimum of 5 years of experience in Data Science, Machine Learning, Applied Science, or a related field.

• Strong foundational knowledge in classical Machine Learning, with practical experience in developing production ML solutions.

• Hands-on experience in data preparation, validation, feature engineering, dataset construction, and model evaluation.

• Proficient in Python with practical experience using PyTorch and/or TensorFlow.

• Extensive practical experience with AWS SageMaker beyond notebook usage, including model training, deployment, inference, pipelines, or production operations.

• Hands-on familiarity with Amazon Bedrock and contemporary Generative AI methodologies.

• Practical experience in model fine-tuning and an understanding of when fine-tuning is more advantageous than prompting, RAG, or other methods.

• Experience in at least one ML domain like NLP, Computer Vision, recommendation systems, forecasting, structured ML, or multimodal ML.

• Knowledge of RAG, embeddings, prompt engineering, foundation models, and agentic workflows.

• Strong understanding of MLOps and production ML practices, encompassing model deployment, monitoring, reproducibility, lifecycle management, and CI/CD.

• Proven ability to design and manage solutions independently rather than solely working from predefined technical specifications.

• Excellent customer-facing communication skills, with the ability to clearly articulate technical trade-offs.

• Capability to navigate ambiguity, messy real-world data, and evolving customer requirements.

• Strong technical judgment and a practical 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.

• Familiarity with Small Language Models or domain-specific model adaptation is a plus.

• Experience in recommendation systems, audio ML, signal processing, or multimodal systems is a plus.

• Background in technical consulting, pre-sales, or customer discovery is a plus.

• AWS Machine Learning certifications are advantageous.

• A Master’s degree or PhD in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related discipline is a plus.

• CV must be submitted in English.

🏝️ Benefits

• Sponsored AWS certifications.

• Access to internal “Expert-led” knowledge sharing sessions.

• Early access to beta AWS features.

• Defined career paths and clear progression from engineering to leadership roles.

• A collaborative, supportive, and transparent workplace culture.

• Opportunities for mentorship and collective success.

• A chance to work with cutting-edge AWS, AI, DevOps, FinOps, and Kubernetes technologies.

• The opportunity to engage in large-scale, meaningful projects with startups and scaleups.

• Global reach with the stability of a mature, profitable company combined with the agility of a startup.

• An equal-opportunity workplace.