Senior AI/ML Engineer (Cloud & Platform) with Strong Python - Raleigh, NC

ACCOUNTABILITIES:

• Design, develop, and deliver scalable solutions that integrate data, machine learning, and application logic
• Build and enhance AI-driven capabilities, including agentic workflows, LLM-based applications, and intelligent automation systems
• Develop and deploy solutions on AWS, ensuring scalability, reliability, and cost efficiency
• Contribute to platform engineering efforts, including improving developer experience, deployment pipelines, and infrastructure reuse
• Translate business problems into end-to-end solutions, including data exploration, feature engineering, modeling, and system integration
• Contribute to system and solution architecture to ensure scalability, reliability, and maintainability
• Partner with product, engineering, and business stakeholders to define and deliver impactful solutions
• Lead or contribute to moderately complex, cross-functional initiatives
• Write and review detailed technical specifications for system and data components
• Ensure adherence to best practices across coding, testing, data quality, and deployment
• Resolve complex technical and data-related issues across systems and models
• Mentor junior and mid-level team members across development, data, and AI practices
• Collaborate with internal and external technology resources to deliver solutions
• Stay current with emerging technologies in AI, cloud, and software systems, and recommend adoption where appropriate
• Support continuous improvement of development processes, data pipelines, and platform capabilities
• Serve as a key technical contributor and escalation point within the team

QUALIFICATIONS:

• 7+ years of experience building and delivering production-grade software, data, or AI solutions
• 2 3 years of hands-on experience in AI/ML systems, including agentic workflows, LLM integrations, or intelligent automation
• Hands-on experience with AWS cloud services and cloud-native architectures
• BS in Engineering, Computer Science, Data Science, or related field required; advanced degree preferred

TECHNICAL SKILLS:

• Strong knowledge of software development methodologies (Agile, Waterfall) and modern engineering best practices
• Strong programming skills in languages such as Python, Java/J2EE, JavaScript, SQL, and modern frameworks
• Experience designing and building scalable systems, including APIs, microservices, and distributed architectures
• Proficiency in CI/CD pipeline design, implementation, and maintenance, including automated testing, deployment, and release management
• Experience with Git-based workflows and GitHub-based automation, including GitHub Actions and GitHub agent capabilities
• Experience with AWS cloud services (e.g., EC2, S3, Lambda, RDS, EKS/ECS) and building cloud-native applications
• Understanding of cloud architecture patterns, including scalability, resiliency, and cost optimization
• Platform engineering experience, including building and maintaining reusable infrastructure, developer platforms, and self-service tooling (e.g., Infrastructure as Code using Terraform/CloudFormation)
• Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes)
• Proficiency in designing and implementing AI-powered systems, including LLM-based applications, agent frameworks, and workflow orchestration
• Experience developing, evaluating, and deploying machine learning models (supervised, unsupervised, or deep learning)
• Strong understanding of statistical analysis, experimentation, and model evaluation techniques
• Experience with data processing and analysis tools (e.g., Pandas, Spark, or equivalent)
• Advanced proficiency in data modeling, data manipulation, and optimization techniques
• Strong knowledge of normalized and dimensional data modeling principles
• Solid understanding of multiple data storage systems (relational, NoSQL, and data lake architectures)
• Familiarity with MLOps practices, including model lifecycle management, monitoring, and observability
• Knowledge of test-driven development and modern testing strategies across both data and application layers
• Strong understanding of prompt engineering, evaluation strategies, and reliability considerations for AI systems
• Strong research and problem-solving skills, including evaluating and applying emerging technologies
• Ability to design, document, and implement complex system and data components
• Strong communication skills, with the ability to work effectively across technical and non-technical audiences