Robotics Prcocess Automation, LLC
ROBOTIC PROCESS AUTOMATION LLC is an equal opportunity employer inclusive of female, minority, disability and veterans, (M/F/D/V). Hiring, promotion, transfer, compensation, benefits, discipline, termination and all other employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity, age, disability, national origin, citizenship/immigration status, veteran status or any other protected status. ROBOTIC PROCESS AUTOMATION LLC will not make any posting or employment decision that does not comply with applicable laws relating to labor and employment, equal opportunity, employment eligibility requirements or related matters. Nor will ROBOTIC PROCESS AUTOMATION LLC require in a posting or otherwise U.S. citizenship or lawful permanent residency in the U.S. as a condition of employment except as necessary to comply with law, regulation, executive order, or federal, state, or local government contract
Responsibilities
Collaborate with Data Scientists and Platform Engineers to build scalable ML and data systems
Develop and maintain high-performance, distributed data pipelines and machine learning infrastructure
Convert machine learning models into production-ready services and APIs
Automate model training, validation, deployment, and inferencing pipelines
Design fault-tolerant, secure, and cost-effective cloud-native architectures using AWS services
Implement Infrastructure as Code (IaC) using CloudFormation, Terraform, or AWS CDK
Build CI/CD pipelines for deployment and testing automation
Establish software engineering best practices including modularity, reusability, test coverage, and documentation
Requirements
5+ years of professional experience developing backend systems using Python
Strong background in building and optimizing large-scale data pipelines and distributed systems
Experience deploying machine learning models and managing ML workflows in production
Solid understanding of software engineering principles including design patterns, version control, unit testing, and CI/CD
Familiarity with object-oriented and functional programming practices
Experience developing RESTful APIs and microservices
Proven experience with cloud engineering on AWS, especially services like SageMaker, Glue, Lake Formation, and Athena
Infrastructure as Code experience using CloudFormation, Terraform, or AWS CDK
Experience with graph technologies and knowledge graphs (e.g., Neo4j, Amazon Neptune) is a plus
Preferred Qualifications
Prior experience working with machine learning operations (MLOps) or AI platform teams
Knowledge of knowledge graph construction and semantic modeling
Background in big data environments and real-time data processing frameworks
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Responsibilities
Collaborate with Data Scientists and Platform Engineers to build scalable ML and data systems
Develop and maintain high-performance, distributed data pipelines and machine learning infrastructure
Convert machine learning models into production-ready services and APIs
Automate model training, validation, deployment, and inferencing pipelines
Design fault-tolerant, secure, and cost-effective cloud-native architectures using AWS services
Implement Infrastructure as Code (IaC) using CloudFormation, Terraform, or AWS CDK
Build CI/CD pipelines for deployment and testing automation
Establish software engineering best practices including modularity, reusability, test coverage, and documentation
Requirements
5+ years of professional experience developing backend systems using Python
Strong background in building and optimizing large-scale data pipelines and distributed systems
Experience deploying machine learning models and managing ML workflows in production
Solid understanding of software engineering principles including design patterns, version control, unit testing, and CI/CD
Familiarity with object-oriented and functional programming practices
Experience developing RESTful APIs and microservices
Proven experience with cloud engineering on AWS, especially services like SageMaker, Glue, Lake Formation, and Athena
Infrastructure as Code experience using CloudFormation, Terraform, or AWS CDK
Experience with graph technologies and knowledge graphs (e.g., Neo4j, Amazon Neptune) is a plus
Preferred Qualifications
Prior experience working with machine learning operations (MLOps) or AI platform teams
Knowledge of knowledge graph construction and semantic modeling
Background in big data environments and real-time data processing frameworks
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