AI Engineer

We are seeking a highly skilled AI Engineer with expertise in Large Language Models (LLMs) and AWS-based AI solutions. The ideal candidate will provide expert guidance on designing, fine-tuning, and deploying LLMs while implementing scalable and cost-efficient AI solutions. This role requires a deep understanding of AWS cloud services, model optimization techniques, and integration of AI capabilities into enterprise applications.

Key Responsibilities:

• Provide expert guidance on designing, fine-tuning, and deploying Large Language Models (LLMs) for various AI applications.
• Advise on and implement scalable and cost-efficient AI solutions using AWS services such as SageMaker, Lambda, EC2, S3, Bedrock, DynamoDB, API Gateway, and CloudFormation.
• Develop and recommend APIs and microservices to integrate LLMs into existing applications using AWS-native solutions.
• Optimize model performance for inference speed, accuracy, and cost-effectiveness by leveraging AWS Auto Scaling and distributed computing solutions.
• Collaborate with data engineers to preprocess, label, and manage large datasets for training and fine-tuning using AWS Glue and AWS Data Pipeline.
• Provide insights on monitoring and maintaining AI applications in production using AWS CloudWatch, AWS X-Ray, and AWS Security services.
• Research and experiment with the latest advancements in LLMs and cloud computing to improve performance within AWS infrastructure.
• Develop robust methodologies for evaluating model accuracy, bias detection, and performance benchmarking.
• Implement automated testing and validation pipelines to ensure consistent model accuracy and reliability using AWS SageMaker Clarify and Model Monitor.
• Work closely with cross-functional teams including data scientists, software engineers, and product managers to design robust AWS-based AI architectures.

Required Technical Skills:

• Strong experience with Large Language Models (LLMs) and AI model deployment.
• Hands-on expertise with AWS AI/ML services, including SageMaker, Bedrock, and Lambda.
• Experience in developing and optimizing APIs and microservices for AI applications.
• Proficiency in Python, TensorFlow, PyTorch, or other ML frameworks.
• Knowledge of distributed computing and auto-scaling strategies for AI workloads.
• Familiarity with MLOps practices, model monitoring, and validation pipelines.
• Experience with data engineering tools like AWS Glue, AWS Data Pipeline, and DynamoDB.
• Strong understanding of cloud security, cost optimization, and AI governance.