Jumio
identity verification
SDEIII-MLOps
Neural analysis suggests this role is
optimal for Senior candidates.
“SDE III - MLOps at Jumio. Skills: MLOps, model serving infrastructure, CI/CD pipelines for ML models, inference performance optimization, AWS serverless architectures, Python. develop the blueprint for highly scalable and performant ML model serving. drive the continuous improvement of the infrastructure and applications to manage the lifecycle of ML assets (data, models)”
What You'll Achieve.
better developer experience; strengthen governance capabilities; maximize inference performance; optimizing costs; eradicate online identity fraud, money laundering and other financial crimes
Industry & Context.
problem solving; complexity analysis; analytical skills; conceptual skills
What They're Looking For.
Must Have
5+ years of experience in software engineering with Python, Experience with model lifecycle management (MLFlow, Weights & Biases or equivalent), Experience with data management ecosystem (quality, transformation, catalog), Experience with ML frameworks, particularly PyTorch, Experience optimizing ML models with hardware acceleration (AWS Neuron, ONNX, TensorRT), Proven experience building and operating AWS serverless architectures, Deep understanding of event-driven processing patterns, SQS/SNS and serverless caching solutions, Experience with containerization using Docker, Orchestration tools knowledge, Knowledge of RESTful API design and implementation, Proficiency in writing good quality & secure code, Familiarity with static code analysis tools, Excellent analytical, conceptual and communication skills in spoken and written English, Experience applying Computer Science fundamentals in algorithm design, problem solving, and complexity analysis
Nice to Have
Experience with model compilation and quantization, Experience with performance profiling and benchmarking ML inference systems, Experience working in regulated industries with strict compliance requirements for cloud-native solutions
What You'll Do.
develop the blueprint for highly scalable and performant ML model serving
drive the continuous improvement of the infrastructure and applications to manage the lifecycle of ML assets (data
design and implement robust ML infrastructure for model deployment
work on efficient CI/CD pipelines for ML models
leverage advanced compilers or hardware optimization to maximize inference performance while optimizing costs
Upgrade ML assets (models
data) management systems for better developer experience and robust governance capabilities
Build and optimize model serving infrastructure with a focus on inference latency and cost optimization
Architect efficient inference pipelines that balance latency
and cost across various acceleration options
Implement cost-efficient
enterprise-scale solutions
Evaluate and implement new technologies and tools
Contribute to architectural decisions for distributed ML systems
How You'll Work.
Team & Collaboration
Collaborate in a cross-functional, distributed team for continuous system improvement; Work with MLEs, QA Engineers, and DevOps Engineers
Communication Scope
Excellent communication skills in spoken and written English; Articulate and persuasive
Full Job Description
Role Purpose: At Jumio, you will work for one of the market leaders in the global identity verification space that is helping to make the digital world a safer place for everyone. As a Software Development Engineer in the MLOpsTeam, you will develop the blueprint for highly scalable and performant ML model serving. Role Value: As a Software Engineer (SDE III), you will drive the continuous improvement of the infrastructure and applications to manage the lifecycle of ML assets (data, models) to better developer experience and strengthen governance capabilities. Secondly, you will design and implement robust ML infrastructure for model deployment, serving, and optimization. You will work on efficient CI/CD pipelines for ML models and leverage advanced compilers or hardware optimization to maximize inference performance while optimizing costs. We welcome you to challenge us to impact our software development processes and tools. Example Responsibilities: Upgrade ML assets (models, data) management systems for better developer experience and robust governance capabilities Build and optimize model serving infrastructure with a focus on inference latency and cost optimization Architect efficient inference pipelines that balance latency, throughput, and cost across various acceleration options Implement cost-efficient, enterprise-scale solutions Collaborate in a cross-functional, distributed team for continuous system improvement Work with MLEs, QA Engineers, and DevOps Engineers Evaluate and implement new technologies and tools Contribute to architectural decisions for distributed ML systems Experience and Qualifications: 5+ years of experience in software engineering with Python Experience with model lifecycle management (MLFlow, Weights & Biases or equivalent) Experience with data management ecosystem (quality, transformation, catalog) Experience with ML frameworks, particularly PyTorch Experience optimizing ML models with hardware acceleration (AWS Neuron , ONNX, Tenso
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