SimpliSolve

Job Description

SimpliSolve is seeking a talented and experienced Machine Learning Engineer proficient in both model development and deployment within the AWS ecosystem. The ideal candidate will have a strong background in machine learning algorithms, data engineering, and cloud computing, particularly AWS services. As a member of our team, you will play a crucial role in designing, developing, and deploying machine learning models to address complex business challenges for our clients.

Responsibility

• Collaborate with cross-functional teams including data scientists, software engineers, and domain experts to understand project requirements and translate them into scalable machine learning solutions.
• Design and implement data pipelines for data ingestion, preprocessing, feature engineering, and model training using AWS services like S3, Glue, and SageMaker.
• Deploy machine learning models into production environments on AWS infrastructure, ensuring scalability, reliability, and performance.
• Monitor model performance and implement continuous integration and deployment (CI/CD) pipelines for automated testing and deployment.
• Collaborate with DevOps teams to manage containerization, orchestration, and infrastructure as code (IaC) using Docker, Kubernetes, and AWS CloudFormation.
• Stay updated with the latest advancements in machine learning technologies, AWS services, and best practices, and integrate them into our development process to enhance efficiency and innovation.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field.
• Proven experience (5 years) working as a Machine Learning Engineer, Data Scientist, or related role, with a focus on model development and deployment.
• Strong proficiency in machine learning algorithms, statistical analysis, and data manipulation techniques.
• Hands-on experience with AWS services such as S3, EC2, Lambda, Glue, SageMaker, and AWS CloudFormation.
• Proficiency in programming languages such as Python, R, or Scala, and libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, and Apache Spark.
• Experience with version control systems (e.g., Git), containerization (e.g., Docker), and CI/CD tools (e.g., Jenkins, Travis CI).
• Excellent problem-solving skills, attention to detail, and ability to work in a fast-paced, collaborative environment.
• Strong communication skills and ability to effectively communicate technical concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience with big data technologies such as Apache Hadoop, Apache Spark, or AWS EMR.
• Certification in AWS (e.g., AWS Certified Machine Learning Specialty, AWS Certified Solutions Architect).
• Knowledge of software development methodologies such as Agile or Scrum.