Senior AI Engineer

Overview

Emmes Group: Building a better future for us all.

Emmes Group is transforming the future of clinical research, bringing the promise of new medical discovery closer within reach for patients. Emmes Group was founded as Emmes more than 47 years ago, becoming one of the primary clinical research providers to the US government before expanding into public-private partnerships and commercial biopharma. Emmes has built industry leading capabilities in cell and gene therapy, vaccines and infectious diseases, ophthalmology, rare diseases, and neuroscience.

We believe the work we do will have a direct impact on patients’ lives and act accordingly. We strive to build a collaborative culture at the intersection of being a performance and people driven company. We’re looking for talented professionals eager to help advance clinical research as we work to embed innovation into the fabric of our company. If you share our motivations and passion in research, come join us!

Primary Purpose

We are looking for a Senior AI Engineer with expertise in AWS-based AI/ML solutions to join our team. In this role, you will be responsible for automating the AI lifecycle, from data preparation, model training, and deployment to monitoring and integration within a fully AWS-driven environment. You will work closely with data engineers, data scientists, software engineers, and product managers to develop and deploy scalable Generative AI and NLP solutions using AWS services.

Responsibilities

• Develop and deploy machine learning models using Generative AI, NLP, and Large Language Models (LLMs) in an AWS ecosystem.
• Utilize AI framework (Lang Chain, Llama Index etc), Bedrock, and SageMaker to develop and integrate AI-driven agents and language models.
• Automate and optimize end-to-end AI workflows, including data processing, model training, deployment, inference and monitoring using AWS Lambda, EC2, S3, SageMaker, and Bedrock.
• Architect and implement scalable and cost-effective AI solutions using AWS infrastructure best practices.
• Optimize AI workloads for high performance and cost-efficiency in AWS environments.
• Monitor model performance, retrain, and optimize ML pipelines for scalability and efficiency.
• Work closely with data engineers and software developers to integrate AI models into AWS-native applications.
• Document technical implementations and communicate best practices across teams.
• Stay up to date with AWS AI/ML advancements and emerging AI trends to enhance system performance and capabilities.
  
  

Qualifications

• Engineering/master’s degree in computer science, Data Science, AI, or a related field.
• 4+ years of experience in Machine Learning, AI engineering, and model deployment in AWS environments.
• Strong expertise in AWS AI/ML services, including SageMaker, Bedrock, Lambda, S3, and EC2.
• Experience in Generative AI, NLP, Computer Vision and LLM-based solutions using Python and associated libraries.
• Hands-on experience in fine-tuning pre-trained CNN/LLM models and building them from scratch.
• Experience with MLOps, CI/CD pipelines, and model monitoring in AWS.
• Proficiency in ML frameworks like TensorFlow, PyTorch, Keras, and scikit-learn.
• Solid understanding of cloud-native AI architectures, infrastructure optimization, and security best practices.
• Strong problem-solving and analytical skills with the ability to work in a cross-functional team.
• Excellent communication skills for technical and non-technical stakeholders.
• Engineering/master’s degree in computer science, Data Science, AI, or a related field.
• 3+ years of experience in Machine Learning, AI engineering, and model deployment in AWS environments.
• Strong expertise in AWS AI/ML services, including SageMaker, Bedrock, Lambda, S3, and EC2.
• Experience in Generative AI, NLP, Computer Vision and LLM-based solutions using Python and associated libraries.
• Hands-on experience in fine-tuning pre-trained CNN/LLM models and building them from scratch.
• Experience with MLOps, CI/CD pipelines, and model monitoring in AWS.
• Proficiency in ML frameworks like TensorFlow, PyTorch, Keras, and scikit-learn.
• Solid understanding of cloud-native AI architectures, infrastructure optimization, and security best practices.
• Strong problem-solving and analytical skills with the ability to work in a cross-functional team.
• Excellent communication skills for technical and non-technical stakeholders.
  
  

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