This is a contract position with hourly rate with hybrid work presence in Mclean, VA. We are seeking an experienced AI Engineer skilled in developing and deploying machine learning models on AWS infrastructure, with expertise in Natural Language Processing (NLP), Generative AI, Large Language Models (LLMs), and Computer Vision. This role will leverage AWS’s AI and machine learning services, such as SageMaker and Bedrock, alongside deep learning frameworks like PyTorch. You’ll work closely with cross-functional teams to design, build, and scale AI solutions that meet evolving business needs and deliver impactful results. Responsibilities: • Design, build, and deploy machine learning models on AWS, with a focus on NLP, Gen AI, LLMs, and Computer Vision applications. • Write clean, production-grade Python code for AI projects, utilizing tools like Poetry, PDM, and Pytest for project management and testing. • Work closely with Data Engineering and DevOps teams to ensure models are robust, scalable, and seamlessly integrated into production workflows. • Build and optimize data processing and ML pipelines, leveraging AWS services to ensure high performance and reliability. • Use Terraform or AWS CDK to implement IaC, streamlining deployments and ensuring infrastructure consistency. • Stay current with advancements in machine learning, Generative AI, NLP, LLMs, Computer Vision, and AWS tools, integrating the latest innovations into projects. • Document processes, models, and workflows, ensuring reproducibility and efficient knowledge transfer across teams. Required Skills: • Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field. Ph.D. in AI/ML is a plus. • 8+ years of experience developing and deploying AI/ML models, of which 6+ years on AWS. • Proficiency in Python, including project management tools (Poetry, PDM) and testing frameworks like Pytest. • Experience with deep learning frameworks like PyTorch and other machine learning libraries such as XGBoost and scikit-learn. • Strong knowledge of data processing, feature engineering, and data pipeline tools (e.g., Spark, Pandas). • Familiarity with CI/CD for ML, model versioning, monitoring, and Infrastructure as Code tools (Terraform, AWS CDK) and practices. • Strong understanding of AWS machine learning services, including SageMaker, Bedrock, and related tools. • Solid foundation in statistics, probability, linear algebra, and machine learning theory. Preferred Qualifications: • Experience with generative models like GANs, VAEs, or diffusion models. • Experience with additional AWS services and architectures, such as Lambda, Glue, and Step Functions. • Knowledge of graph databases or reinforcement learning. • Contributions to open-source AI/ML projects or published research in relevant fields.