We are seeking an experienced
Machine Learning Engineer to design, build, and deploy scalable data and ML pipelines on AWS. The role focuses on
real‑time data processing,
streaming architectures, and
end‑to‑end ML lifecycle management using modern cloud‑native technologies.
The ideal candidate will have strong experience across
AWS services,
streaming data platforms, and
production‑grade ML systems, with hands‑on expertise in
SageMaker and PyTorch.
Job Description
- Redis cluster setup
- Kafka/Flink streaming pipelines
- S3 Data pipeline
- Real time micro batches implementation (5 minutes, hourly, daily)
- Mongo/Atlas as alternative implementation (we might land with S3 instead)
- SageMaker MLOps / SageMaker Training / SM Model Deployment
- Pytorch
Design machine learning systems: You will work on building and implementing machine learning models and deploying these models into production.
Data analysis: You will be responsible for improving data quality through data cleaning, validation, and transformation so that it can be used effectively by the machine learning models.
Educate the team: As our machine learning expert, you will also have the opportunity to teach others about machine learning principles and help them understand how these principles can be applied to our products.
Stay updated: You should stay abreast of latest trends and developments in machine learning, ensuring we continue to innovate.
Qualifications
Bachelor's Degree in Computer Science, Statistics, Applied Math or related field.
8+ years of practical experience with machine learning, algorithm design, data modeling, and software development.
Hands-on experience in machine learning, predictive modeling and analysis, and cross-functional collaboration.
Proficient in Python, R, Java or C++ programming languages.
Experience with Hadoop, Hive, Spark, SQL or other big data technologies.
Excellent communication skills, as this role will collaborate with both technical and non-technical colleagues.
