Sr. AI/ML Developer

Abstract:

A Machine Learning (ML) Developer is an expert in applying advanced AI/ML algorithms and techniques to solve complex problems, including building, training, and deploying machine learning models. This role focuses on creating and optimizing systems to automate processes such as image classification, speech recognition, market forecasting, and large language model (LLM) fine-tuning.

• Roles & Responsibilities
• 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
• Programming and Frameworks
• Fundamentals of SQL
• FastAPI framework
• PyTorch framework
• MMDetection framework
• Parallel Processing and Optimization
• Techniques for parallel execution and data processing
• Image and Data Processing
• Optical Character Recognition (OCR)
• Data processing and image manipulation
• Deep Learning Concepts
• Basics of neural networks and optimizers
• Convolutional Neural Networks (CNN)
• Region-based Convolutional Neural Networks (RCNN)
• Advanced AI and LLMs
• Prompt engineering principles
• Retrieval-Augmented Generation (RAG) using the LangChain framework
• Open-source Large Language Models (LLMs)
• MLOps and Deployment
• MLOps practices, including:
• MLflow
• Docker
• CI/CD pipelines
• GitLab
• 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.