Job Description

Responsibilities:

• Develops scripts and software that integrate ML models into larger systems
• Selects the most appropriate ML libraries and frameworks based on specific use cases
• Optimizes the training process for ML models, ensuring efficiency and scalability
• Implements robust APIs for model inference, making models available for real-time or batch predictions
• Designs data structures that optimize model performance and data
  processing speed
• Manages compute resources effectively to ensure models train and run efficiently
• Sets up logging systems and generates key metrics to monitor model and system performance
• Conducts hyperparameter tuning and evaluates different models to find the best fit for the task
• Monitors for and mitigates model degradation and drift in production environments
• Deploys models in a way that ensures reliability and scalability in production
• Creates CI/CD pipelines to automate the model lifecycle, from development to production updates

Job Requirements

Requirements:

• Degree in Computer Science, Engineering, AI/ML, or a related field (or equivalent experience)
• Strong object oriented software development skills, with a focus on machine learning applications
• Mastery of ML-relevant libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Expertise in selecting the right tools and frameworks based on project needs
• Knowledge of building and optimizing ML pipelines, including parallelization techniques
• Experience in deploying models through APIs for inference and real-time usage
• Familiarity with data structures and compute architectures that enhance model performance
• Ability to implement logging systems and generate performance metrics
• Knowledge of hyperparameter tuning, model selection, and training processes
• Experience with monitoring model degradation and drift in production
• Expertise in creating and managing CI/CD pipelines for ML model deployment
• Familiarity with cloud platforms (e.g., AWS, GCP) and distribute computing environments
• Excellent communication and teamwork skills to collaborate across technical and business teams
• Primary Skills:
• Core Libraries: Python 3.11+, pandas, NumPy, scikit-learn
• Deep Learning: TensorFlow, Keras, PyTorch
• Natural Language Processing: (Azure) OpenAI, Prompt Engineering, transformers, Langchain
• Development & Deployment: Git, Docker
• Data Visualization & Application: Streamlit, Matplotlib
• Azure: Azure Cognitive Services, Azure Databricks
• Secondary Skills:
• Azure Databases & Data Services: Azure Cosmos DB, Azure Data Factory

Skills