Gen AI Architect

Job Description:
We are seeking a highly skilled GenAI Lead to join our growing team. In this role, you will leverage your expertise in Generative AI, machine learning, and natural language processing (NLP) to design and implement innovative AI-driven solutions. The ideal candidate will have a deep understanding of architectural design, data strategies, AI agents, and the latest advancements in language models. You will play a key role in delivering cutting-edge solutions, working with large-scale data, and building systems that enhance automation, intelligence, and efficiency for our clients. Key Responsibilities: GenAI Solutioning & Architectural Design: Lead the design and implementation of end-to-end Generative AI solutions, ensuring scalable, robust, and efficient architecture that aligns with business needs. Data Strategy & Management: Develop and execute strategies for data collection, cleaning, transformation, and integration for AI model development. Apply best practices for data pipeline design and optimization. AI Agents Development: Design and implement intelligent AI agents using advanced techniques in NLP and machine learning to improve automation, user interaction, and decision-making processes. Fine-tuning & Prompt Engineering: Fine-tune pre-trained models (e.g., GPT, BERT, etc.) and optimize prompt engineering techniques to drive high-quality, actionable outputs for diverse business use cases. Machine Learning & Deep Learning Models: Build, train, and deploy machine learning models, including deep learning models, for complex AI applications across various domains. LLM Provisioning from CSPs: Lead the provisioning and customization of Large Language Models (LLMs) from major Cloud Service Providers (CSPs) such as Azure, AWS, and Google Cloud, ensuring optimal performance and cost-effectiveness.Technologies & Frameworks: Utilize Langchain, CrewAI, and VectorDB for building, integrating, and scaling NLP solutions, and leverage Flask/FastAPI for model deployment and integration. Database & SQL Management: Work with databases (SQL/NoSQL) for data storage, querying, and management to support AI model development and deployment. Tokenization & NLP Techniques: Apply advanced tokenization techniques to preprocess and analyze data for NLP tasks, enhancing model efficiency and performance. Deployment & Evaluation: Oversee the deployment of AI models, ensuring smooth integration with production systems, and perform rigorous evaluation of LLMs for accuracy, efficiency, and scalability. Required Skills & Qualifications: Proven experience in understanding GenAI solutions and architectural design for large-scale AI systems. Expertise in data cleaning, transformation, and data strategy to drive AI and machine learning initiatives. Strong hands-on experience with AI agents, prompt engineering, and fine-tuning LLMs for business applications. Proficiency in machine learning models, deep learning techniques, and NLP applications. Deep knowledge of LLM provisioning from different Cloud Service Providers (CSPs) (e.g., AWS, Azure, Google Cloud). Experience with Langchain, CrewAI, VectorDB, Flask, FastAPI, and related frameworks for model integration and deployment.trong database skills, with proficiency in SQL and experience in working with NoSQL databases.