Senior Engineer, AI/ML Software

Duties

• Design, develop and maintain scalable and efficient Artificial Intelligence/Machine Learning (“AI/ML”) systems that solve complex problems.
• Collaborate with cross-functional teams, including product managers, UX designers, and software engineers to gather requirements and design appropriate solutions.
• Lead the development of end-to-end AI/ML pipelines, including data preprocessing, model training, prompt engineering, and performance evaluation.
• Participate in code reviews, architecture discussions, and technical planning.
• Optimize AI/ML model performance and ensure robustness, fairness, and explainability.
• Monitor AI/ML system performance, troubleshoot issues, and provide timely resolutions.
• Implement data security and privacy measures to protect sensitive information.
• Continuously evaluate and recommend improvements to our data infrastructure, tools, and processes.
• Stay up to date with emerging trends and technologies in the fields of AI and ML.
• Mentor junior engineers and contribute to best practices in AI/ML development.

Requirements

• Must have a Master’s degree in Computer Science, Computer Engineering, Software Engineering, Data Science, Data Engineering, Analytics or closely related technical discipline (willing to accept foreign education equivalent).
• Three (3) years of experience in the proffered job or related occupation assessing and deploying AI/ML solutions to meet business needs.
• Must possess the following (quantitative experience requirements not applicable to this section):
  • DE building modern AI software architectures with microservices, serverless functions, and fault-tolerant distributed systems, and deploying these systems in cloud platforms such as Azure, AWS, and GCP.
  • DE applying agile development, SOLID principles, design patterns, version control, code review, debugging, documentation, linting, unit/integration testing, and CI/CD software engineering best practices to create well-designed software solutions.
  • DE using Python and complementary languages including JavaScript and C/C++.
  • DE using frameworks including LangChain, LangGraph, LlamaIndex, Pytorch, scikit-learn, and XGBoost to train, evaluate, and optimize generative AI and predictive ML models, and to create agentic AI systems that can solve complex problems through dynamic tool usage, reasoning, and deep thought.
  • DE using SQL, NoSQL, and vector database systems, such as PostgreSQL and pgvector, to create optimized storage backends for RAG applications.
  • DE building applications with containerization technologies such as Docker and Kubernetes.