Key Responsibilities:

• Advanced Model Development:
• Design and implement complex models using machine learning and deep learning, particularly focusing on natural language processing (NLP), named entity recognition (NER), and large language models (LLMs) such as BERT.
• Create complex AI agent using Langchain, llamaIndex, auto-gen
• Create Retrival Augmented Generation (RAG) Applications
• Multi-agent workflows and bots
• Deep Learning and Neural Network Expertise
• Apply a deep theoretical understanding of neural networks to develop sophisticated models that are both efficient and scalable.
• Cross-Domain Integration
• Incorporate knowledge of image processing to integrate multi-modal systems that leverage both text and visual data.
• Research, Documentation, and Reporting
• writing
• Conduct ongoing research to stay ahead of new trends and techniques in machine learning, deep learning, and AI.
• Creating custom APIs using frameworks such as FAST-API, Django, Flask
• Have experince deploying ML models into production
• Training LLMs with LoRA and QLoRA
• experience with Quantization
• Containerizing code via docker
• Setting up MLOps pipelines with frameworks such as Data version control (DVC)
• Creating Ci/CD pipelines
• Image segmentation
• Object detection
• facial recognition

Qualifications:

• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or a related field.
• Experience:
• 4 – 6 years of experience in ML model development, including hands-on experience with NLP, NER, and LLMs.
• Experience with neural network design and implementation.
• Technical Skills:
• Proficiency in Python, with good knowledge of ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience in developing and implementing models using BERT, RAG, LangChain, and other advanced ML techniques.
• Familiarity with image processing techniques
• Deep theoretical understanding of how models work, including underlying mathematical principles.
• Document methodologies and innovations thoroughly; prepare detailed reports and presentations for technical and non-technical stakeholders.
• Collaborate with cross-functional teams to ensure models are well-integrated into the company’s product ecosystem.
• Optimize neural network architectures and parameters for improved performance across various tasks.
• Utilize techniques like Language Model Chaining and Retriever-Augmented Generation (RAG) to enhance text-based applications.

Nice to have :

• experience with auto scaling
• experience with GANs
• experience with stable diffusion based models
• experience with 3D neural networks
• experience with Meta learning
• worked on Raspberry PI or embedding systems and deployed ML model onto them before.

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