Design and deploy scalable machine learning solutions using AWS services, collaborate with teams, mentor others, and ensure best practices in data and ML lifecycle.
As a Machine Learning Architect on our Professional Services team, you will be responsible for designing, deploying, and optimizing data-driven machine learning solutions on AWS. You’ll work closely with clients to architect secure, scalable ML systems, build and manage datasets, lead model development and deployment, and implement MLOps pipelines. This role requires strong experience in both cloud-native ML services and modern data architecture. You’ll serve as a trusted advisor to clients and mentor team members in best practices across the data and ML lifecycle.
Responsible for:
· Designing, implementing, and maintaining end-to-end ML architecture using AWS services
· Leading data acquisition, cleaning, and transformation workflows using AWS Glue and Lambda
· Building scalable data pipelines to feed ML models with high-quality, production-grade data
· Collaborating with data engineers and scientists to optimize model input/output processes
· Deploying and managing models using SageMaker endpoints, pipelines, and the model registry
· Selecting appropriate AWS storage and database services (S3, RDS, Redshift) to support ML use cases
· Developing automated workflows for model training, evaluation, and retraining using Step Functions and MLOps best practices
· Assisting clients in migrating legacy ML solutions to cloud-native platforms
· Troubleshooting data pipeline and model deployment issues
· Participating in project planning, client meetings, and delivery reviews
· Contributing to internal R&D projects that evaluate new AWS ML and data services
· Mentoring junior team members on data modeling, ML deployment, and data architecture best practices
· Remaining up to date with ML, AI, and data technology trends
· Advising clients on responsible AI practices, data governance, and compliance in model development
How you will be successful:
· Championing a “data-first, machine learning-enabled” mindset
· Demonstrating deep analytical thinking and creative problem-solving
· Delivering accurate, explainable, and business-relevant ML solutions
· Becoming a subject matter expert in AWS ML services within 9–12 months
· Building strong relationships with data engineers, analysts, and business stakeholders
· Maintaining curiosity around ML research, trends, and production strategies
· Always be learning
What experience you need:
· 5+ years of professional IT or software engineering experience
· 2+ years of hands-on AWS experience in ML and data workloads
· At least one AWS Certification (preferably Machine Learning – Specialty or Solutions Architect – Professional)
· Experience with Amazon SageMaker: model training, hosting, custom containers, and Pipelines
· Proficiency with SageMaker Studio for end-to-end ML development
· Strong knowledge of AWS data services:
· Amazon S3 for storing training datasets and artifacts
· AWS Glue for ETL and data cataloging
· Amazon RDS/Aurora and Redshift for structured data and analytics
· Familiarity with streaming data and batch processing using Lambda, Step Functions, or Kafka
· Proficiency in Python and frameworks such as Scikit-Learn, TensorFlow, PyTorch, and Pandas
· Experience with NLP and CV services like Amazon Comprehend and Rekognition
· Strong SQL skills and familiarity with both relational and NoSQL data stores
· Knowledge of data modeling, dimensional modeling, and building feature stores
· Experience designing and implementing MLOps workflows, CI/CD, and monitoring practices
· Understanding of data privacy, model drift, bias detection, and explainability techniques
· Bonus: Experience working with big data platforms like Apache Spark, EMR, or Lake Formation
Top Skills
Amazon Rds
Amazon Redshift
Amazon S3
Spark
AWS
Aws Glue
Aws Lambda
Aws Sagemaker
Emr
Pandas
Python
PyTorch
Scikit-Learn
SQL
TensorFlow
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