AI Specialist

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

Requirements

• 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.