Job
Description
We are looking for an experienced Senior Generative AI Architect with 6+ years of experience to lead the design, development, and deployment of cutting-edge AI solutions. The ideal candidate will have deep expertise in Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI/ML architecture. This role requires strategic thinking, hands-on implementation skills, and the ability to collaborate with cross-functional teams to drive AI innovation. Key Responsibilities: Architect and Lead AI Solutions: Design end-to-end architectures for AI and machine learning solutions tailored to business needs. AI Model Development: Develop scalable and maintainable frameworks for AI models, pipelines, and systems. Vector Search & Data Retrieval: Implement and optimize document retrieval, semantic search, and contextual query answering using VectorDBs (e.g., Pinecone, Weaviate, FAISS). Prompt Engineering & Optimization: Design and refine advanced prompting techniques to enhance AI model efficiency. LangGraph & GenAI Frameworks: Utilize LangGraph, LangChain, HuggingFace, OpenAI APIs, and other AI frameworks for efficient workflow orchestration. MLOps & AI Deployment: Architect systems for deploying AI models into production (e.g., using Docker, Kubernetes, or serverless technologies) and ensure CI/CD pipelines for automated testing and deployment. AI Infrastructure & Cloud Integration: Define technology stacks, tools, and infrastructure for AI solutions, including cloud-based and on-premise systems. Scalability & Performance: Ensure solutions are scalable and capable of handling high throughput and low-latency requirements. Data Engineering Collaboration: Work closely with data engineers to design robust data pipelines for training and inference. AI Ethics & Governance: Ensure AI solutions adhere to responsible AI principles, compliance, and security best practices. Cross-functional Collaboration: Work closely with data scientists, engineers, and business stakeholders to integrate AI capabilities into products and services. Continuous Innovation: Explore new tools and techniques to improve existing systems, prototype innovative solutions, and assess their feasibility for production. Technical Leadership & Mentorship: Mentor junior engineers, data scientists, and developers in best practices, establishing coding standards, design principles, and operational guidelines for AI projects. Code Review & Best Practices: Review code to ensure it follows best practices and is performant. Technical Communication: Effectively communicate technical concepts to non-technical stakeholders. Stay Updated: Keep up with the latest trends, frameworks, and breakthroughs in AI and machine learning. Required Skills: AI/ML Architecture: Expertise in architecting scalable AI/ML solutions, focusing on Generative AI and LLMs. LLM Fine-Tuning & Optimization: Strong experience in pre-training, fine-tuning, and optimizing large-scale AI models. Vector Databases: Proficiency with Pinecone, Weaviate, FAISS, or similar for efficient document storage and retrieval. GenAI Frameworks: Hands-on experience with LangGraph, LangChain, HuggingFace, OpenAI API, and other Generative AI tools. Cloud & DevOps: Strong knowledge of AI deployments on AWS, GCP, or Azure using Kubernetes, Docker, and Terraform. NLP & RAG Techniques: Expertise in transformer models, embeddings, tokenization, and retrieval-augmented generation workflows. Prompt Engineering: Deep understanding of prompt crafting, tuning, and optimization for LLM performance enhancement. MLOps & CI/CD: Experience in ML model lifecycle management, version control, and automated deployment. Problem-Solving & Leadership: Strong analytical skills with the ability to solve complex AI challenges and mentor teams. Preferred Skills: Experience in multi-modal AI (text, vision, audio integration). Knowledge of ethical AI frameworks and AI governance best practices. Familiarity with real-time AI inference optimization techniques. Hands-on experience with distributed AI computing and GPU acceleration. Understanding of data privacy laws and AI compliance regulations. Qualifications: Education: Bachelors or Master s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field. Experience: 6+ years of hands-on experience in AI/ML, with a focus on Generative AI and LLM-based architectures. Certifications: Relevant certifications in AI/ML, cloud computing (AWS, GCP, Azure), or MLOps are a plus. Publications & Contributions: Contributions to AI research, open-source projects, or patents in AI/ML are highly desirable.
