Technical Specialist-AI Development

4 days ago

Mumbai Metropolitan Region, India Birlasoft Full time ₹ 1,04,000 - ₹ 1,30,878 per year

Area(s) of responsibility

Job Description
Key Responsibilities:

  • Design, develop, and maintain scalable, efficient, and reliable systems to support GenAI and machine learning-based applications and use cases
  • Lead the development of data pipelines, architectures, and tools to support data-intensive projects, ensuring high performance, security, and compliance
  • Collaborate with other stakeholders to integrate AI and ML models into production-ready systems
  • Work closely with non-backend expert counterparts, such as data scientists and ML engineers, to ensure seamless integration of AI and ML models into backend systems
  • Ensure high-quality code, following best practices, and adhering to industry standards and company guidelines

Hard Requirements

  • Senior backend engineer with a proven track record of owning the backend portion of projects
  • Experience collaborating with product, project, and domain team members
  • Strong understanding of data pipelines, architectures, and tools
  • Proficiency in Python (ability to read, write and debug Python code with minimal guidance)

Mandatory Skills

  • Machine Learning: experience with machine learning frameworks, such as scikit-learn, TensorFlow, or PyTorch
  • Python: proficiency in Python programming, with experience working with libraries and frameworks, such as NumPy, pandas, and Flask
  • Natural Language Processing: experience with NLP techniques, such as text processing, sentiment analysis, and topic modeling
  • Deep Learning: experience with deep learning frameworks, such as TensorFlow, or PyTorch
  • Data Science: experience working with data science tools
  • Backend: experience with backend development, including design, development, and deployment of scalable and modular systems
  • Artificial Intelligence: experience with AI concepts, including computer vision, robotics, and expert systems
  • Pattern Recognition: experience with pattern recognition techniques, such as clustering, classification, and regression
  • Statistical Modeling: experience with statistical modeling, including hypothesis testing, confidence intervals, and regression analysis

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