What are the responsibilities and job description for the Senior AI/ML Engineer (Cloud & Platform) with Strong Python - Raleigh, NC position at CA-One Tech Cloud Inc.?
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
Salary : $60