Chief AI Software Architect

3 days ago

Bellary, Karnataka, India beBeeSoftware Full time US$ 1,80,000 - US$ 2,50,000

AI Software Engineer

About the Role

We're seeking a highly skilled AI Software Engineer with a strong foundation in machine learning, graph theory, SQL, and generative AI.

The ideal candidate will design, develop, and deploy robust, scalable solutions, collaborating with cross-functional teams to drive innovation in AI-driven applications.

Main Responsibilities:
  • Machine Learning Model Development: Design, implement, and fine-tune machine learning models using Python libraries such as TensorFlow, PyTorch, or scikit-learn to solve real-world problems.
  • Graph-Based Algorithm Development: Build and optimize graph-based algorithms and data structures using libraries like NetworkX or PyG (PyTorch Geometric) for applications such as network analysis, fraud detection, or knowledge graphs.
  • Data Management: Write complex SQL queries to manage, transform, and analyze large datasets, ensuring efficient data pipelines and integration with databases like PostgreSQL, MySQL, or SQLite.
  • Generative AI Development: Design and implement generative AI models (e.g., LLMs, GANs, or VAEs) using frameworks like Hugging Face, LangChain, or custom solutions for tasks such as text generation, data augmentation, or synthetic data creation.
  • Code Optimization: Write clean, modular, and maintainable Python code, adhering to best practices, and optimize for performance and scalability in production environments.
  • Collaboration and Innovation: Work closely with data scientists, engineers, and business teams to translate requirements into technical solutions, contributing to architectural decisions and innovative AI strategies.
  • Data Pipeline Development: Build and maintain ETL pipelines to preprocess and integrate data for machine learning and graph-based applications, using tools like Apache Airflow or Pandas.
  • Model Deployment: Deploy machine learning and generative AI models to production environments using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure).
  • Research and Stay Current: Stay updated on advancements in machine learning, graph theory, and generative AI, applying cutting-edge techniques to enhance project outcomes.

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