Job Description: We are seeking a skilled and enthusiastic AI/LLM Engineer to join our team and help design, build, and deploy advanced AI systems using cutting-edge large language models (LLMs). The role will involve working with state-of-the-art technologies such as Langchain, Vector Databases, and Generative AI to create scalable solutions that drive real-world impact. You will play a key role in fine-tuning, integrating, and deploying LLMs across diverse applications, ranging from chatbots to content generation, search, and beyond. Key Responsibilities: • Develop and Fine-tune LLMs: Work with language models such as GPT, BERT, T5, and others to fine-tune them for specific use cases like natural language understanding, text generation, and conversation systems. • Langchain Integration: Utilize Langchain to create pipelines and frameworks that integrate LLMs with external data sources, APIs, and tools, enabling more dynamic and contextually aware AI applications. • Vector Databases: Implement and manage vector databases (e.g., Pinecone, FAISS, Weaviate) to store and retrieve high-dimensional vector embeddings for tasks like semantic search, document retrieval, and knowledge-based query answering. • Research & Experimentation: Stay at the forefront of AI and NLP research to experiment with new techniques and methodologies. Innovate on model fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) techniques. • Model Deployment &MLOps: Build and deploy LLM-powered solutions in production environments, ensuring performance optimization and scalability. Leverage MLOps practices to automate the end-to-end lifecycle of models, from training to deployment and monitoring. • Data Management & Preprocessing: Handle large datasets for training and inference, including data collection, preprocessing, and augmentation. Develop and apply tokenization techniques and manage large-scale text data. • Collaborate on AI Solutions: Work closely with cross-functional teams, including data scientists, machine learning engineers, and product developers, to translate business problems into technical solutions using LLMs and AI. • Optimize Vector Search: Develop efficient vector-based search solutions that allow for semantic retrieval of information using embeddings generated from language models. • Prompt Engineering: Experiment with prompt engineering and retrieval-based models to improve the contextual relevance and accuracy of language models for various tasks, such as question answering, document retrieval, and interactive agents. • API Integration: Integrate LLMs with external APIs (OpenAI, Hugging Face, etc.) for diverse applications, ensuring smooth interoperability across systems. • Computer Vision: Work with computer vision models and techniques, applying image processing and deep learning approaches to solve vision-related tasks (e.g., object detection, image generation, or scene understanding). Integrate computer vision capabilities with LLM-based applications for multi-modal solutions. • Traditional Machine Learning: Apply traditional machine learning algorithms (e.g., Random Forest, SVM, Logistic Regression) where appropriate. Develop end-to-end ML solutions for predictive analytics, classification, regression, and clustering tasks in both structured and unstructured data contexts. Required Qualifications: • Educational Background: Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related fields. • Experience with LLMs: 2+ years of experience working with large language models (e.g., GPT, BERT, T5, etc.) and related AI/NLP technologies. • Langchain Expertise: Hands-on experience building AI workflows using Langchain for enhanced integration between models and external data sources. • Vector Database Knowledge: Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate) and vector search techniques for high-dimensional data retrieval. • NLP and Transformer Models: Strong understanding of NLP concepts and transformer-based architectures for language modeling, text processing, and embedding generation. • Programming Languages: Proficiency in Python, with extensive experience using frameworks like TensorFlow, PyTorch, and Hugging Face. • Cloud Platforms: Experience deploying AI models on cloud platforms (AWS, GCP, Azure) and managing resources for large-scale model training and inference. • MLOps& Deployment: Practical experience with MLOps practices and tools to manage the lifecycle of machine learning models in production environments.
Key Responsibilities: • Develop and Fine-tune LLMs: Work with language models such as GPT, BERT, T5, and others to fine-tune them for specific use cases like natural language understanding, text generation, and conversation systems. • Langchain Integration: Utilize Langchain to create pipelines and frameworks that integrate LLMs with external data sources, APIs, and tools, enabling more dynamic and contextually aware AI applications. • Vector Databases: Implement and manage vector databases (e.g., Pinecone, FAISS, Weaviate) to store and retrieve high-dimensional vector embeddings for tasks like semantic search, document retrieval, and knowledge-based query answering. • Research & Experimentation: Stay at the forefront of AI and NLP research to experiment with new techniques and methodologies. Innovate on model fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) techniques. • Model Deployment &MLOps: Build and deploy LLM-powered solutions in production environments, ensuring performance optimization and scalability. Leverage MLOps practices to automate the end-to-end lifecycle of models, from training to deployment and monitoring. • Data Management & Preprocessing: Handle large datasets for training and inference, including data collection, preprocessing, and augmentation. Develop and apply tokenization techniques and manage large-scale text data. • Collaborate on AI Solutions: Work closely with cross-functional teams, including data scientists, machine learning engineers, and product developers, to translate business problems into technical solutions using LLMs and AI. • Optimize Vector Search: Develop efficient vector-based search solutions that allow for semantic retrieval of information using embeddings generated from language models. • Prompt Engineering: Experiment with prompt engineering and retrieval-based models to improve the contextual relevance and accuracy of language models for various tasks, such as question answering, document retrieval, and interactive agents.
Required Qualifications: • Educational Background: Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related fields. • Experience with LLMs: 2+ years of experience working with large language models (e.g., GPT, BERT, T5, etc.) and related AI/NLP technologies. • Langchain Expertise: Hands-on experience building AI workflows using Langchain for enhanced integration between models and external data sources. • Vector Database Knowledge: Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate) and vector search techniques for high-dimensional data retrieval. • NLP and Transformer Models: Strong understanding of NLP concepts and transformer-based architectures for language modeling, text processing, and embedding generation. • Programming Languages: Proficiency in Python, with extensive experience using frameworks like TensorFlow, PyTorch, and Hugging Face. • Cloud Platforms: Experience deploying AI models on cloud platforms (AWS, GCP, Azure) and managing resources for large-scale model training and inference. • MLOps& Deployment: Practical experience with MLOps practices and tools to manage the lifecycle of machine learning models in production environments
