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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist (SageMaker, Bedrock) - **Company:** AUTOMATE I.T. - **Location:** United States (Remote available) - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Data Analysis, Computer Vision, Continuous Integration, Data Cleansing, Information Engineering, DevOps, Python (Programming Language), Machine Learning, Language Modeling, Natural Language Processing, Recommender Systems, Tensorflow, Azure Machine Learning, Signal Processing, Feature Engineering, Pytorch, Prompt Engineering, Model Validation, Generative AI, AI Platforms, Information Technology, Machine Learning Operations - **Published:** September 5, 2026 - **Apply:** https://arc.dev/remote-jobs/j/redirect/pho5lus9zg ## About the Role * 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. * Deep practical experience working with data, including 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 fine-tuning models and understanding when fine-tuning is preferable to prompting, RAG, or other approaches. * Experience in at least one strong ML domain such as NLP, Computer Vision, recommendation systems, forecasting, structured ML, multimodal ML, or similar. * 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. Nice to have * Experience with AgentCore, Bedrock Agents, LangGraph, Strands Agents, or other agentic frameworks. * Experience with Small Language Models or domain-specific model adaptation. * Experience with recommendation systems, audio ML, signal processing, or multimodal systems. * Experience in technical consulting, pre-sales, or customer discovery. * AWS Machine Learning certifications. * Master's degree or PhD in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field. ## Description * Own Data Science projects end-to-end: Take customer problems from technical discovery through data analysis, solution design, experimentation, implementation, deployment, and production validation. * Choose the right technical approach: Evaluate whether a problem should be solved with prompt engineering, RAG, GenAI, agentic workflows, fine-tuning, smaller language models, classical ML, Computer Vision, recommendation systems, or custom model training. * Work deeply with data: Analyze and prepare customer datasets, identify data quality issues, create representative validation and golden datasets, and ensure the available data can support the chosen modelling approach. * Build and fine-tune ML models: Train, fine-tune, optimize, evaluate, and deploy models using Python, PyTorch/TensorFlow, SageMaker, and modern ML tooling. * Work with Generative AI: Build and evaluate GenAI solutions using Amazon Bedrock, RAG, prompt engineering, model selection, agentic workflows, and related AWS-native AI services. * Use SageMaker in production: Work with SageMaker Studio, training jobs, endpoints, pipelines, model registry, batch inference, monitoring, and other production ML capabilities. * Design production-ready solutions: Make architecture decisions across quality, latency, cost, scalability, maintainability, observability, and operational complexity. * Work directly with customers: Participate in technical discovery, workshops, architecture discussions, and delivery conversations with founders, CTOs, engineering teams, and technical stakeholders. * Challenge technical assumptions: Help customers avoid unnecessary complexity, explain trade-offs, costs and recommend simpler or more effective approaches when appropriate. * Lead through technical ownership: Independently own projects with minimal supervision and mentor less experienced Data Scientists and engineers when needed. * * Collaborate across teams: Work closely with AI Engineers, MLOps, Data Engineering, DevOps, and Solution Architecture teams on customer solutions that cross multiple technical domains. ## Related Videos - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Photonic Computing: Programming a New Class of AI Accelerators (incl. Live Coding)](https://www.wearedevelopers.com/videos/100196-photonic-computing-programming-a-new-class-of-ai-accelerators-incl-live-coding) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) - [How We Built a Machine Learning-Based Recommendation System (And Survived to Tell the Tale)](https://www.wearedevelopers.com/videos/752-how-we-built-a-machine-learning-based-recommendation-system-and-survived-to-tell-the-tale) ## Related Articles - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Got AI ideas but no money? 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