Benefits:

LONGTERM

HYBRID

SKILL development

Opportunity for advancement

Job Title: Data Scientist – AWS Cloud Location: Dallas, TX (Hybrid – 3 days onsite per week) Interview Process: Virtual Tech Screen → In-Person Interview

Join Us in Building the Future of AI in the Cloud Are you a data science enthusiast who thrives at the intersection of machine learning, cloud innovation, and real-world impact? We’re seeking a Data Scientist with deep AWS cloud experience to develop and deploy cutting-edge AI/ML solutions at scale.

You’ll be part of a forward-thinking team focused on solving complex challenges using advanced models, large datasets, and cloud-native tools. If you’re passionate about pushing the boundaries of what’s possible with AI on AWS—this is your opportunity.

Roles & Responsibilities:

Design & Deploy AI/ML Models: Leverage your 6+ years in IT (including 3+ years in data science) to create robust AI/ML solutions using AWS tools.

Model Development: Build, train, and fine-tune deep learning models with TensorFlow and PyTorch on scalable AWS infrastructure.

Data at Scale: Apply your modeling expertise to large, complex datasets with a solid foundation in ML theory and statistical research.

Advanced Techniques: Utilize methods such as supervised/unsupervised learning, reinforcement learning, time series forecasting, Bayesian inference, and optimization.

NLP & LLMs: Build and enhance large language model (LLM) and NLP applications using open-source models and AWS-native tools.

RAG Applications: Independently design Retrieval-Augmented Generation (RAG) applications using AWS services and LLMs.

AWS Optimization: Ensure scalability, efficiency, and performance using AWS tools like SageMaker, Lambda, EC2, S3, and Redshift.

Qualifications:

Python Proficiency: Strong Python skills with a focus on AI/ML development and implementation.AWS ML Tools: Hands-on experience with AWS services such as SageMaker, Lambda, EC2, and others relevant to AI/ML pipelines.

Deep Learning Expertise: Proven work with TensorFlow and PyTorch, especially in cloud environments.

LLM & RAG Experience: Ability to independently develop applications using LLMs and RAG frameworks within AWS.

Cloud-Native Thinking: Familiarity with end-to-end AI/ML workflows in a cloud-first environment, including performance optimization

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Flexible work from home options available.