Job Description: GenAI Developer (LangChain, LangGraph, VectorDB, LLMs, MCP, Embedding, RAG) Position Overview: We are looking for an experienced and motivated GenAI Developer to join our team in building cutting-edge applications powered by GenAI technologies. As a GenAI Developer, you will work with LangChain, LangGraph, Vector Databases, Large Language Models (LLMs), and Retrieval-Augmented Generation (RAG) to create intelligent, scalable, and efficient AI-driven applications. You will play a key role in integrating these advanced technologies into real-world use cases, including text generation, knowledge retrieval, and recommendation systems. Key Responsibilities: LangChain Development: Develop GenAI applications using LangChain, including building chains, agents, and integrations for a variety of use cases. LangGraph Development: Utilize LangGraph for building workflow agents, controlling states, and developing intelligent decision-making systems. Vector Database Integration: Design, implement, and maintain vector database solutions (e.g., Qdrant, Pinecone, FAISS) for efficient storage and retrieval of embeddings. LLM Integration: Leverage LLMs to generate high-quality outputs for various use cases, such as conversational agents, document summarization, or content generation. Embedding Building: Develop embeddings for text, images, and other media to integrate into retrieval systems and enhance search relevance and accuracy. RAG Implementation: Design and implement RAG workflows to improve accuracy and context in AI applications by combining real-time retrieval with language model generation. MCP Integration: Work with the Microsoft Cognitive Platform (MCP) for AI and cognitive services, integrating them with LangChain and LangGraph for enhanced capabilities. Optimization & Fine-Tuning: Continuously optimize AI models, embeddings, and retrievers for performance and efficiency. Collaboration & Problem-Solving: Work closely with product managers, data scientists, and other engineers to understand business needs and deliver AI-driven solutions. Required Skills & Qualifications: Technical Skills: LangChain : Hands-on experience with LangChain for developing intelligent workflows, managing chains, and leveraging its powerful capabilities for building GenAI applications. LangGraph : Proficiency in LangGraph for designing advanced workflows and agents with state management, decision-making, and loops. Vector Databases (e.g., Qdrant, Pinecone, FAISS) : Experience working with vector databases to manage and search embeddings, and optimize large-scale retrieval tasks. Large Language Models (LLMs) : In-depth understanding of LLMs, such as GPT, BERT, and other transformer-based architectures, and how to integrate them into GenAI applications. Embedding Creation and Integration : Expertise in building embeddings using models like sentence-BERT, OpenAI embeddings, or custom models for a variety of data types (text, images, etc.). Retrieval-Augmented Generation (RAG) : Proven experience in designing and implementing RAG systems that combine real-time retrieval with language model generation. Microsoft Cognitive Platform (MCP) : Familiarity with integrating Microsoft Cognitive Services, such as Azure Cognitive Search, Azure OpenAI, or other Microsoft AI tools, into GenAI applications. Model Context Protocol (MCP) - Building or exposing Resources , Prompts , tools to LLM based AI Agents . Natural Language Processing (NLP) : Strong understanding of NLP techniques, including text classification, entity recognition, sentiment analysis, and text summarization. AI Model Optimization : Experience in model fine-tuning, prompt engineering, and optimizing LLMs for specific tasks. APIs & Microservices : Familiarity with designing and consuming RESTful APIs, and microservice architecture for scalable AI solutions. Additional Skills: Cloud Platforms (Azure, AWS, GCP) : Experience deploying GenAI applications on cloud platforms such as Azure, AWS, or Google Cloud, with an emphasis on cloud-based AI solutions. Machine Learning Frameworks : Knowledge of popular ML frameworks, such as TensorFlow, PyTorch, or Hugging Face, and how to use them for model training and inference. Data Engineering : Strong skills in handling large datasets, including data preprocessing, data cleaning, and data augmentation, to support AI model training. Version Control : Proficiency with Git and GitHub for version control, collaborative development, and code review processes. Problem-Solving & Analytical Thinking : Excellent problem-solving skills with a strong analytical mindset to break down complex AI challenges into manageable components. Collaboration & Communication : Strong teamwork and communication skills to effectively collaborate with cross-functional teams and deliver high-impact solutions. Preferred Qualifications: Advanced Degree : A degree in Computer Science, Data Science, AI, or a related field. GenAI Application Development : Experience in building AI-driven applications or platforms with a focus on generative models, search systems, and conversational AI. Familiarity with LlamaIndex, FAISS, or Similar Tools : Knowledge of popular vector search tools and libraries beyond LangChain and LangGraph is a plus.
