AI Specialist

• Machine Learning Development: Design build train and fine-tune machine learning and deep learning models particularly in the context of large language models (LLMs).
  
• Data Pipeline Creation: Develop and manage efficient data pipelines for data preprocessing and feature engineering.
  
• MLOps Implementation: Create CI/CD pipelines to automate the deployment monitoring and updating of ML models in production environments.
  
• LLM Fine-Tuning: Fine-tune LLMs for specific applications and domains leveraging frameworks like Hugging Face and open-source LLMs.
  
• Model Evaluation: Regularly evaluate model performance accuracy and reliability using statistical and computational techniques.
  
• Collaboration: Work in an Agile environment with cross-functional teams to integrate ML solutions into larger systems.
  
• Framework Utilization: Utilize machine learning frameworks such as TensorFlow PyTorch or Keras to develop scalable solutions.
  
• Data Management: Manage large datasets ensure data quality and design robust preprocessing pipelines.
  
• AI/ML Research: Stay updated on the latest advancements in AI/ML algorithms tools and techniques to implement cutting-edge solutions.
  

Requirements

1. Programming and Frameworks

• Fundamentals of SQL
  
• FastAPI framework
  
• PyTorch framework
  
• MMDetection framework
  

2. Parallel Processing and Optimization

• Techniques for parallel execution and data processing
  

3. Image and Data Processing

• Optical Character Recognition (OCR)
  
• Data processing and image manipulation
  

4. Deep Learning Concepts

• Basics of neural networks and optimizer
  
• Convolutional Neural Networks (CNN)
  
• Region-based Convolutional Neural Networks (RCNN)
  

5. Advanced AI and LLMs

• Prompt engineering principles
  
• Retrieval-Augmented Generation (RAG) using the LangChain framework
  
• Open-source Large Language Models (LLMs)
  

6. MLOps and Deployment

• MLflow
  
• Docker
  
• CI/CD pipelines
  
• GitLab
  
  

• AI/ML Expertise: In-depth knowledge of AI/ML algorithms and techniques including data preprocessing feature engineering and model optimization.
  
• MLOps: Experience with end-to-end ML lifecycle management including creating CI/CD pipelines for model deployment.
  
• Frameworks & Libraries: Expertise in machine learning frameworks like TensorFlow PyTorch and Keras and familiarity with libraries for LLMs (e.g. Hugging Face Transformers).
  
• Distributed Systems: Knowledge of distributed computing systems like Hadoop or Spark.
  
• LLM Fine-Tuning: Understanding LLM architectures fine-tuning techniques and methods for evaluating and monitoring LLM performance.
  
• Mathematics: Advanced math skills including linear algebra Bayesian statistics group theory and optimization techniques.
  
• Data Engineering: Proficiency in data modeling data architecture and preprocessing pipelines.
  
• Communication: Strong written and verbal communication skills.
  
• Problem-Solving: Outstanding analytical and problem-solving abilities.
  

Benefits

• 5-day working company.
  
• Quarterly rewards based on roadmap achievements and customers success.
  
• 20 Yearly leaves.
  
• 14 National Holidays Off.
  
• Cross-team work culture.
  
• Career Development & Training Programs.
  
• Employee Referral Benefits.
  
• Birthday/Anniversary/Festival Celebrations.
  
• Compensatory Off Benefits.
  
• Paid half-day leaves on special occasions of Birthdays & anniversaries.
  
• Meals while working extra.
  
• Yearly day-outing activities.
  
• Yearly Achievement Awards.