Data AI Engineer

Description

Key Responsibilities

• Explore and implement generative AI technologies using Large Language Models and other generative models to create innovative solutions.
• Build and maintain robust APIs for enabling Retrieval-Augmented Generation and generative AI agents within business use cases.
• Design, develop, and maintain robust data pipelines for ingestion, processing, and transformation of large datasets.
• Assist our data science team in the development and deployment of traditional machine learning models.
• Collaborate with data scientists to understand model requirements and translate them into scalable engineering solutions.
• Monitor and maintain performance and reliability of deployed APIs, models, and data pipelines.
• Stay up to date with the latest advancements in machine learning, generative AI, and related technologies.
  

Minimum Skills Required :

• Bachelor's Degree in a quantitative discipline (e.g., Computer Science, Mathematics, Statistics, or a related field) with a minimum of 5 years of professional experience; OR
• Master's Degree in a similar field with at least 3 years of relevant experience.
• 5+ years of experience developing Python-based applications intended for use by others, beyond just building models or conducting data analysis.
• Experience with data manipulation libraries (e.g., Pandas, NumPy).
• Familiarity with API frameworks (e.g., FastAPI, Flask) and RESTful API concepts.
• Strong understanding of machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Familiarity with cloud platforms (e.g., AWS, Google Cloud, Azure)
• 3+ years of experience with containerization (e.g., Docker, Kubernetes).
• 2+ years of experience with CI/CD tools, pipelines
• Experience working with Large Language Model (LLM) in application development
• Experience with version control systems (e.g., Git) and software development best practices.
• Experience with Databricks
  

Preferred Qualifications

• Excellent problem-solving skills and ability to work in a collaborative environment.
• Knowledge of generative AI frameworks such as Langchain or Pydantic AI.
• Experience deploying software using tools like Docker and Kubernetes.
• Experience with deep learning techniques and frameworks.
• Knowledge of natural language processing (NLP).
• Familiarity with big data technologies (e.g., Hadoop, Spark).