Money Fit by DRS
Lead/Sr Machine Learning Engineer - AWS (with LLM Focus)
Money Fit by DRS, Myrtle Point, Oregon, United States, 97458
Remote but MST and PST based talent
Responsibilities:LLM-Optimized MLOps Infrastructure:
Design and implement MLOps infrastructure on AWS tailored for LLMs, leveraging services like SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and more.LLM Deployment Pipelines:
Build and manage CI/CD pipelines specifically for LLM deployment, addressing unique challenges like model size, inference optimization, and versioning.LLMOps Practices:
Implement LLMOps best practices for monitoring model performance, drift detection, prompt management, and feedback loops for continuous improvement.RESTful API Development:
Design and develop RESTful APIs to expose LLM capabilities to other applications and services, ensuring scalability, security, and optimal performance.Model Optimization:
Apply techniques like quantization, distillation, and pruning to optimize LLM models for efficient inference on AWS infrastructure.Monitoring and Observability:
Establish comprehensive monitoring and alerting mechanisms to track LLM performance, latency, resource utilization, and potential biases.Prompt Engineering and Management:
Develop strategies for prompt engineering and management to enhance LLM outputs and ensure consistency and safety.Collaboration:
Work closely with data scientists, researchers, and software engineers to integrate LLM models into production systems effectively.Cost Optimization:
Continuously optimize LLMOps processes and infrastructure for cost-efficiency while maintaining high performance and reliability.Qualifications:Experience:
3+ years of experience in MLOps or a related field, with hands-on experience in deploying and managing LLMs.AWS Expertise:
Strong proficiency in AWS services relevant to MLOps and LLMs, including SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and API Gateway.LLM Knowledge:
Deep understanding of LLM architectures (e.g., Transformers), training techniques, and inference optimization strategies.Programming Skills:
Proficiency in Python and experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation), REST API frameworks (e.g., Flask, FastAPI), and LLM libraries (e.g., Hugging Face Transformers).Monitoring:
Familiarity with monitoring and logging tools for LLMs, such as Prometheus, Grafana, and CloudWatch.Containerization:
Experience with Docker and container orchestration (e.g., Kubernetes, ECS) for LLM deployment.Problem Solving:
Excellent problem-solving and troubleshooting skills in the context of LLMs and MLOps.Communication:
Strong communication and collaboration skills to effectively work with cross-functional teams.
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Responsibilities:LLM-Optimized MLOps Infrastructure:
Design and implement MLOps infrastructure on AWS tailored for LLMs, leveraging services like SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and more.LLM Deployment Pipelines:
Build and manage CI/CD pipelines specifically for LLM deployment, addressing unique challenges like model size, inference optimization, and versioning.LLMOps Practices:
Implement LLMOps best practices for monitoring model performance, drift detection, prompt management, and feedback loops for continuous improvement.RESTful API Development:
Design and develop RESTful APIs to expose LLM capabilities to other applications and services, ensuring scalability, security, and optimal performance.Model Optimization:
Apply techniques like quantization, distillation, and pruning to optimize LLM models for efficient inference on AWS infrastructure.Monitoring and Observability:
Establish comprehensive monitoring and alerting mechanisms to track LLM performance, latency, resource utilization, and potential biases.Prompt Engineering and Management:
Develop strategies for prompt engineering and management to enhance LLM outputs and ensure consistency and safety.Collaboration:
Work closely with data scientists, researchers, and software engineers to integrate LLM models into production systems effectively.Cost Optimization:
Continuously optimize LLMOps processes and infrastructure for cost-efficiency while maintaining high performance and reliability.Qualifications:Experience:
3+ years of experience in MLOps or a related field, with hands-on experience in deploying and managing LLMs.AWS Expertise:
Strong proficiency in AWS services relevant to MLOps and LLMs, including SageMaker, EC2 (with GPU instances), S3, ECS/EKS, Lambda, and API Gateway.LLM Knowledge:
Deep understanding of LLM architectures (e.g., Transformers), training techniques, and inference optimization strategies.Programming Skills:
Proficiency in Python and experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation), REST API frameworks (e.g., Flask, FastAPI), and LLM libraries (e.g., Hugging Face Transformers).Monitoring:
Familiarity with monitoring and logging tools for LLMs, such as Prometheus, Grafana, and CloudWatch.Containerization:
Experience with Docker and container orchestration (e.g., Kubernetes, ECS) for LLM deployment.Problem Solving:
Excellent problem-solving and troubleshooting skills in the context of LLMs and MLOps.Communication:
Strong communication and collaboration skills to effectively work with cross-functional teams.
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