AI Engineering Lead

4 days ago Be among the first 25 applicants

• Strong proficiency in Python for AI/ML development.
• Hands‑on experience with OpenAI and modern AI frameworks.
• Experience with AWS services for AI deployment.
• Proven leadership and ability to guide engineering teams.

• Solid understanding of ETL processes and data integration.
• Experience with Airflow and Harness for managing AI pipelines.
• Familiarity with platforms for data management.
• Familiarity with automated testing and deployment pipelines.

• Build and maintain AI/ML pipelines on AWS, leveraging services such as Amazon S3 for data storage, AWS Lambda for serverless functions, and Amazon EC2 for compute resources.
• Deploy trained models into production environments using AWS Sage Maker Endpoints, AWS Lambda, or containerization technologies like Docker and Kubernetes on AWS EKS.
• Implement MLOps practices for continuous integration, continuous delivery (CI/CD), and monitoring of ML models.
• Preprocess and analyze data, engineer features, and select appropriate algorithms for specific problems.
• Utilize AWS services like Amazon Sage Maker for efficient model training, hyperparameter tuning, and experiment tracking.
• Analyze data, build models, and uncover insights, often using Python for exploration and prototyping.

$110,000-$135,000 a year