Senior ML Engineer

As a Senior Machine Learning Engineer Contractor specializing in AWS ML Pipelines, your primary responsibility will be to design, develop, and deploy advanced ML pipelines within an AWS environment. You will work on cutting-edge solutions that automate entity matching for master data management, implement fraud detection systems, handle transaction matching, and integrate GenAI capabilities. The ideal candidate for this role should possess extensive hands-on experience in AWS services like SageMaker, Bedrock, Lambda, Step Functions, and S3. Moreover, you should have a strong command over CI/CD practices to ensure a robust and scalable solution. Your key responsibilities will include designing and developing end-to-end ML pipelines focusing on entity matching, fraud detection, and transaction matching. You will be integrating generative AI solutions using AWS Bedrock to enhance data processing and decision-making. Collaboration with cross-functional teams to refine business requirements and develop data-driven solutions tailored to master data management needs will also be a crucial aspect of your role. In terms of AWS ecosystem expertise, you will be required to utilize SageMaker for model training, deployment, and continuous improvement. Additionally, leveraging Lambda and Step Functions to orchestrate serverless workflows for data ingestion, preprocessing, and real-time processing will be part of your daily tasks. Managing data storage, retrieval, and scalability concerns using AWS S3 will also be within your purview. Furthermore, you will need to develop and integrate automated CI/CD pipelines to streamline model testing, deployment, and version control. Ensuring rapid iteration and robust deployment practices to maintain high availability and performance of ML solutions will be essential. Data security and compliance will be a critical aspect of your role. You will need to implement security best practices to safeguard sensitive data, ensuring compliance with organizational and regulatory requirements. Incorporating monitoring and alerting mechanisms to maintain the integrity and performance of deployed ML models will be part of your responsibilities. Collaboration and documentation will also play a significant role in your day-to-day activities. Working closely with business stakeholders, data engineers, and data scientists to ensure solutions align with evolving business needs will be crucial. You will also need to document all technical designs, workflows, and deployment processes to support ongoing maintenance and future enhancements. Providing regular progress updates and adapting to changing priorities or business requirements in a dynamic environment are expected. To qualify for this role, you should have at least 5+ years of professional experience in developing and deploying ML models and pipelines. Proven expertise in AWS services including SageMaker, Bedrock, Lambda, Step Functions, and S3 is necessary. Strong proficiency in Python and/or PySpark, demonstrated experience with CI/CD tools and methodologies, and practical experience in building solutions for entity matching, fraud detection, and transaction matching within a master data management context are also required. Familiarity with generative AI models and their application within data processing workflows will be an added advantage. Strong analytical and problem-solving skills are essential for this role. You should be able to transform complex business requirements into scalable technical solutions and possess strong data analysis capabilities with a track record of developing models that provide actionable insights. Excellent verbal and written communication skills, the ability to work independently as a contractor while effectively collaborating with remote teams, and a proven record of quickly adapting to new technologies and agile work environments are also preferred qualities for this position. A Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field is a plus. Experience with additional AWS services such as Kinesis, Firehose, and SQS, prior experience in a consulting or contracting role demonstrating the ability to manage deliverables under tight deadlines, and experience within industries where data security and compliance are critical will be advantageous.,