. You will be responsible for designing, developing, deploying, and maintaining scalable ML solutions in a cloud-native environment.
Key Responsibilities:
Design and implement machine learning models and pipelines using
AWS SageMaker
and related services.
Develop and maintain robust
data pipelines
for training and inference workflows.
Collaborate with data scientists, engineers, and product teams to translate business requirements into ML solutions.
Implement
MLOps best practices
including CI/CD for ML, model versioning, monitoring, and retraining strategies.
Optimize model performance and ensure scalability and reliability in production environments.
Monitor deployed models for drift, performance degradation, and anomalies.
Document processes, architectures, and workflows for reproducibility and compliance.
Required Skills & Qualifications:
Strong programming skills in
Python
and familiarity with ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).
Solid understanding of
machine learning algorithms
, model evaluation, and tuning.
Hands-on experience with
AWS ML services
, especially
SageMaker
, S3, Lambda, Step Functions, and CloudWatch.
Experience with
data engineering tools
(e.g., Apache Airflow, Spark, Glue) and
workflow orchestration
.
Proficiency in
MLOps tools
and practices (e.g., MLflow, Kubeflow, CI/CD pipelines, Docker, Kubernetes).
Familiarity with monitoring tools and logging frameworks for ML systems.
Excellent problem-solving and communication skills.
Preferred Qualifications:
AWS Certification (e.g., AWS Certified Machine Learning - Specialty).
Experience with real-time inference and streaming data.
* Knowledge of data governance, security, and compliance in ML systems.
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