Data Integration Lead

Skilled Machine Learning Engineer with strong experience in AWS cloud services, and proven ability in integration and development of ML solutions. The ideal candidate will collaborate closely with data scientists, developers, and DevOps teams to design, deploy, and maintain scalable machine learning systems in production. Key Responsibilities: Design, develop, and deploy machine learning models in production environments using AWS services (e.g., SageMaker, Lambda, EC2, S3, EKS, etc.). Build scalable data pipelines for training and inference using AWS Glue, Step Functions, and other ETL tools. Integrate ML models into existing systems and APIs, ensuring seamless operation across multiple services and platforms. Collaborate with cross-functional teams to gather requirements and implement ML solutions aligned with business goals. Optimize and monitor deployed models for performance, latency, and cost-effectiveness using tools such as CloudWatch, CloudTrail, and Prometheus. Write clean, maintainable code in Python (and optionally Java/Scala) following best software engineering practices. Automate model training, validation, and deployment workflows using CI/CD pipelines (e.g., CodePipeline, Jenkins, GitHub Actions). Ensure security and compliance in data handling and model deployment within the AWS ecosystem. Document ML workflows, APIs, and integration points to support scalability and future maintenance. Stay up to date with the latest developments in ML, cloud services, and integration technologies, and proactively suggest improvements"