GenAI Developer

Job Description

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GenAI Developer (LangChain, LangGraph, VectorDB, LLMs, MCP, Embedding, RAG)

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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.

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Key Responsibilities

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• 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.

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Technical Skills

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Required Skills & Qualifications:

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• 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.

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Additional Skills

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• 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.

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Preferred Qualifications

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• 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.