Senior AI Engineer

Job Title: Senior AI Engineer

Scope: Senior AI Engineer will take the lead in designing, developing, and deploying advanced AI solutions, with a focus on NLP and LLM technologies. This role requires a seasoned professional capable of working on complex AI projects that involve training and fine-tuning large-scale language models, developing NLP pipelines, and deploying these models at scale.

You will collaborate closely with our AI research, data science, and software engineering teams to drive innovation and implement AI solutions that improve customer experiences, optimize operations, and fuel business growth.

Responsibilities:

• Lead AI Projects: Design, develop, and implement advanced AI solutions focused on NLP and LLM technologies to solve real-world problems.
• Model Development: Build, fine-tune, and deploy large-scale language models (such as GPT, BERT, etc.) for various applications including text classification, question answering, summarization, machine translation, and chatbots.
• NLP Pipeline: Develop and optimize NLP workflows such as tokenization, text preprocessing, and feature extraction using libraries like spaCy, NLTK, or Hugging Face.
• Model Training & Fine-Tuning: Train custom LLMs and NLP models, leverage transfer learning, and optimize models for inference speed and accuracy.
• Performance Optimization: Implement scalable solutions that optimize the performance of LLMs in production environments, including managing large datasets and improving model throughput.
• Collaboration: Work cross-functionally with data scientists, machine learning engineers, and software engineers to integrate AI models into our platform.
• Research & Innovation: Stay up-to-date with the latest advancements in NLP and AI, and incorporate state-of-the-art techniques into our products.
  
  

Mentorship: Mentor junior team members and provide technical leadership in AI-related projects.

Qualifications:

• Experience: 2:4 years of experience in AI, with a strong focus on NLP and LLM.
• Experience with reinforcement learning (RL) for NLP tasks.
• Familiarity with multimodal models and techniques.
• Knowledge of data annotation and labeling tools.
• Contributions to AI research in academic journals or conferences.
• Experience with MLOps practices to streamline model deployment and monitoring
  
  

Benefits:

• Competitive salary.
• Health insurance.
• Professional development opportunities.
• Flexible working hours.
• Collaborative and supportive work environment.
  
  

Working Conditions:

Working Hours: 9 AM -6 PM ( Sunday-Thursday ).

Working Model: Hybrid