Lead I - ML Engineering

Core Responsibilities:

• The MLE will design, build, test, and deploy scalable machine learning systems,

optimizing model accuracy and efficiency

• Model Development: Algorithms and architectures span traditional statistical methods to

deep learning along with employing LLMs in modern frameworks.

• Data Preparation: Prepare, cleanse, and transform data for model training and

evaluation.

• Algorithm Implementation: Implement and optimize machine learning algorithms and

statistical models.

• System Integration: Integrate models into existing systems and workflows.
• Model Deployment: Deploy models to production environments and monitor

performance.

• Collaboration: Work closely with data scientists, software engineers, and other

stakeholders.

• Continuous Improvement: Identify areas for improvement in model performance and

systems.
Skills:

• Programming and Software Engineering: Knowledge of software engineering best

practices (version control, testing, CI/CD).

• Data Engineering: Ability to handle data pipelines, data cleaning, and feature

engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for
troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch,
Neo4J graph

• Model Deployment and Monitoring: MLOps Experience in deploying ML models to

production environments.

• Knowledge of model monitoring and performance evaluation.

Required experience:

• Amazon SageMaker: Deep understanding of SageMaker's capabilities for building,

training, and deploying ML models; understanding of the Sagemaker pipeline with ability
to analyze gaps and recommend/implement improvements

• AWS Cloud Infrastructure: Familiarity with S3, EC2, Lambda and using these services in

ML workflows

• AWS data: Redshift, Glue
• Containerization and Orchestration: Understanding of Docker and Kubernetes, and their

implementation within AWS (EKS, ECS)