As an AI/ML AWS Solutions Architect, you won’t just be drawing diagrams—you will be a hands-on builder. You will lead the design, development, and deployment of machine learning and generative AI solutions on AWS. You will bridge the gap between our clients' business goals and technical execution, guiding them through the complexities of data prep, model training, MLOps, and inference at scale.
If you love solving complex problems, working with cutting-edge AWS services (like SageMaker and Bedrock), and mentoring fellow engineers, you’ll fit right in with our architecture team.
● Architect & Design: Lead technical workshops with clients to understand their business challenges and design secure, scalable AI/ML architectures on AWS.
● Build & Implement: Roll up your sleeves and build proof-of-concepts (PoCs) and production implementations. We expect our architects to be hands-on keyboard, no Powerpoint architect.
● MLOps Engineering: Design and implement automated CI/CD pipelines for machine learning models using Amazon SageMaker Pipelines, AWS Step Functions, and standard DevOps tools.
● Generative AI: Help clients navigate the new world of Large Language Models (LLMs) by designing solutions using Amazon Bedrock, RAG (Retrieval-Augmented Generation) architectures, and vector databases.
● Consult & Guide: Act as a trusted advisor to clients, explaining complex ML concepts in plain English, (and Czech or Slovak) and ensuring best practices for security, cost optimization, and performance.
● Team Leadership: Mentor junior data engineers and developers, helping elevate the overall technical bar of the professional services team.
Must-Haves:
● Experience: 3+ years of hands-on experience designing and implementing cloud solutions, with at least 1-2 years specifically focused on AWS AI/ML services (Medior to Senior level).
● AWS ML Ecosystem: Deep, practical knowledge of Amazon Bedrock, SageMaker and managed AI services (e.g., Comprehend, Rekognition, Textract).
● Programming: Strong proficiency in Python. You should be comfortable writing clean, production-ready code.
● Cloud Fundamentals: Solid grasp of core AWS services (IAM, S3, VPC, Lambda, API Gateway) and how to secure ML workloads within a multi-account AWS environment.
● Infrastructure as Code (IaC): Experience deploying infrastructure using AWS CDK, Terraform, or CloudFormation.
● Consulting Chops: Excellent communication skills. You need to be able to present complex technical architectures to both C-level executives and technical teams.
Nice-to-Haves (Bonus Points):
● Certifications: AWS Certified Machine Learning – Associate, AWS Certified Data Engineer - Associate, AWS Certified Solutions Architect – Associate.
● Generative AI: Hands-on experience with Amazon Bedrock, LangChain, LlamaIndex, or building RAG pipelines.
● Data Engineering: Familiarity with AWS data services like Glue, Athena, and Redshift.
● Agency/Consultancy Background: Previous experience working in a professional services or client-facing agency environment.
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