Gathers, analyzes, and transforms large datasets to support AI/ML model development and evaluation
Implements natural language processing, generative AI, and other ML technologies in production systems
Collaborates with data scientists to productionize machine learning models and integrate with backend services
Contributes to AI/ML communities of practice through demos, tech talks, and knowledge sharing
Required qualifications, capabilities, and skills
Formal training or certification in software engineering and 3+ years applied experience
Hands-on experience with AI/ML model development, training, and deployment
Proficient in Python and ML frameworks (TensorFlow, PyTorch, scikit-learn)
Proficient in Java for both client-side and server-side application development
Proficient in HTML, CSS, and modern front-end web development practices
Experience with data pipelines and processing frameworks (Spark, Pandas, NumPy)
Solid understanding of CI/CD, MLOps practices, and model versioning
Demonstrated knowledge of cloud platforms (AWS, Azure, GCP) and containerization
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Preferred qualifications, capabilities, and skills
Experience with generative AI and large language models (LLMs)
Familiarity with vector databases and RAG architectures
Experience with containerization (Docker, Kubernetes) and cloud-native deployments
Knowledge of NLP techniques and transformer architectures
