Job
Description
We are seeking a passionate and driven Generative AI Engineer to design, develop, and deploy innovative AI applications using Large Language Models (LLMs). The ideal candidate will have hands-on experience with prompt engineering, Python programming, and foundational knowledge of AI orchestration frameworks like LangChain and LangGraph. Experience with the Model Context Protocol (MCP) is a significant advantage. You will work closely with senior AI engineers and cross-functional teams to bring cutting-edge generative AI solutions to life Role & responsibilities Generative AI Application Development: Design, build, and deploy AI applications leveraging LLMs for various use cases such as RAG (Retrieval-Augmented Generation) based chatbots, intelligent agents, content generation, and more. Prompt Engineering: Develop, test, and optimize advanced prompt engineering techniques (e.g., few-shot, Chain-of-Thought, ReAct) to enhance LLM performance, accuracy, and user experience. LLM Orchestration with LangChain & LangGraph: Utilize LangChain and LangGraph to build robust, stateful, and multi-agent AI systems, managing complex conversational flows and integrating external tools and data sources. Python Development: Write clean, efficient, and well-documented Python code for AI model integration, data preprocessing, API development, and general application logic. Model Context Protocol (MCP): Implement and leverage MCP for seamless interaction between LLMs and external systems, ensuring efficient context management and secure communication (if applicable to project needs). Model Evaluation & Testing: Assist in evaluating the performance of generative AI models, defining relevant metrics, and conducting rigorous testing to ensure reliability and effectiveness. Collaboration: Work collaboratively with data scientists, machine learning engineers, product managers, and other stakeholders to translate business requirements into technical solutions. Research & Innovation: Stay updated with the latest advancements in generative AI, LLMs, and related frameworks, and actively experiment with new approaches to improve existing solutions. Deployment & MLOps (Basic): Contribute to the deployment of AI models and understand basic MLOps practices for model versioning, monitoring, and continuous improvement. . Required Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related quantitative field. 1-3 years of hands-on experience in machine learning, software development, or data science, with exposure to AI/ML projects. Strong proficiency in Python programming and familiarity with relevant libraries (e.g., NumPy, Pandas). Proven experience with LangChain for building LLM-powered applications and chaining together various components. Familiarity with LangGraph for orchestrating complex, stateful multi-agent workflows. Hands-on experience with prompt engineering and optimizing prompts for LLMs. Understanding of Large Language Models (LLMs) and their underlying concepts. Experience or strong understanding of Model Context Protocol (MCP) for connecting LLMs with external tools and data sources. Excellent problem-solving skills and a proactive approach to challenges. Strong communication and interpersonal skills, with the ability to articulate technical concepts clearly.) Preferred Skills : Experience with cloud platforms (Azure, AWS, GCP) for deploying AI/ML solutions. Microsoft Certified: Azure AI Engineer Associate (AI-102) or similar AI/ML certification. Familiarity with vector databases (e.g., Pinecone, Chroma, FAISS). Experience with deep learning frameworks such as TensorFlow or PyTorch. Knowledge of Natural Language Processing (NLP) techniques beyond LLMs. Experience in building conversational AI systems or chatbots
