Senior Data Scientist (SageMaker, Bedrock)

• 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.

• 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.

Automat-it is an all-in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands-on expertise in AI, DevOps, and FinOps to empower fast-paced startups to grow, deliver & win. Our customers save significant time-to-market and optimize their cloud performance and costs.

We work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech.

We’re looking for a Senior Data Scientist to join our Data Science practice and work directly with customers on complex AI and Machine Learning projects on AWS.

This is not a traditional analytics or research-focused Data Science role. You’ll work across classical ML, fine-tuning, GenAI, multimodal systems, and production ML on AWS, helping customers decide which technical approach actually fits their problem.

You’ll own projects end-to-end: understand the customer’s domain and data, evaluate different solution paths, define the architecture, implement the critical parts, and guide delivery through production., Be at the Forefront of Cloud Innovation

Join a global leader and AWS Premier Tier Services Partner. We don’t just use the cloud; we master it. As an All-In AWS partner, you’ll work with the most sophisticated tech stacks and help maintain our status as a top-tier collaborator in the global ecosystem.

Work on Meaningful, Large-Scale Impact

We specialize in helping startups and scaleups scale at speed. By joining us, you’ll be part of a team that has successfully completed hundreds of complex projects, directly influencing the growth of the next generation of unicorns through our unique “Product-based Professional Services” approach.

Accelerate Your Career Trajectory

In our fast-paced, dynamic environment, “growth” isn’t just a buzzword. You will have:

• Clear Progression: Defined career paths from engineering to leadership.
• Continuous Learning: Sponsored AWS certifications and access to internal “Expert-led” knowledge sharing.
• Cutting-Edge Exposure: Early access to beta AWS features and high-level architectural challenges.

Thrive in a Culture of Excellence

We pride ourselves on a collaborative, supportive, and transparent culture. You’ll work alongside and learn from some of the industry’s brightest architects and DevOps engineers who are committed to mentorship and collective success, including AWS Gold Jacket recipients.

Global Reach, Boutique Feel

With over 250 employees across EMEA and the USA, enjoy the stability of a mature, profitable company with the agility and spirit of a startup. Whether you are working on GenAI & Agentic use cases, DevOps automation, FinOps optimization, or Kubernetes migrations, your voice is heard, and your contributions are visible.

Automat-it is committed to fostering a workplace that promotes equal opportunities for all. We firmly believe that cultivating a diverse workforce is crucial to our success. Our recruitment decisions are grounded in your experience and skills, recognizing the value you bring to our team.