Urgent Hiring MLOps Engineer with AWS_Remote

Job Position : Machine Learning Operations (MLOps) Engineer - AWS (with LLM Focus)

Job Location : Remote

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.