Sr AIML Developer

• Machine Learning Development: Design build train and finetune 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 FineTuning: Finetune LLMs for specific applications and domains leveraging frameworks like Hugging Face and opensource LLMs.
  
• Model Evaluation: Regularly evaluate model performance accuracy and reliability using statistical and computational techniques.
  
• Collaboration: Work in an Agile environment with crossfunctional 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 cuttingedge solutions.
  

Requirements

1. Programming and Frameworks

Fundamentals of SQL

FastAPI framework

PyTorch framework

MMDetection framework

2. Parallel Processing and Optimization

Techniques for parallel 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 optimizers

Convolutional Neural Networks (CNN)

Regionbased Convolutional Neural Networks (RCNN)

5. Advanced AI and LLMs

Prompt engineering principles

RetrievalAugmented Generation (RAG) using the LangChain framework

Opensource Large Language Models (LLMs)

6. MLOps and Deployment

MLOps practices including:

MLflow

Docker

CI/CD pipelines

GitLab

• AI/ML Expertise: Indepth knowledge of AI/ML algorithms and techniques including data preprocessing feature engineering and model optimization.
  
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• Programming: Strong skills in Python R Java C C# TypeScript and JavaScript.
  
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• MLOps: Experience with endtoend ML lifecycle management including creating CI/CD pipelines for model deployment.
  
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• Frameworks & Libraries: Expertise in machine learning frameworks like TensorFlow PyTorch and Keras and familiarity with libraries for LLMs (e.g. Hugging Face Transformers).
  
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• Distributed Systems: Knowledge of distributed computing systems like Hadoop or Spark.
  
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• LLM FineTuning: Understanding LLM architectures finetuning techniques and methods for evaluating and monitoring LLM performance.
  
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• Mathematics: Advanced math skills including linear algebra Bayesian statistics group theory and optimization techniques.
  
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• Data Engineering: Proficiency in data modeling data architecture and preprocessing pipelines.
  
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• Communication: Strong written and verbal communication skills.
  

• ProblemSolving: Outstanding analytical and problemsolving abilities.
  

Benefits

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