AI ML Architect

As an AI Architect at our company, your role will involve designing, building, and deploying enterprise-grade AI solutions with expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Vertex AI platform. You will be responsible for architecting LLM-based solutions, designing RAG frameworks, leveraging Vertex AI, building scalable AI pipelines, and integrating AI models with enterprise systems. Additionally, you will mentor cross-functional teams, define AI governance practices, and evaluate new technologies to enhance our AI platform capabilities. Key Responsibilities: - Architect and lead the development of LLM-based solutions using open-source and proprietary models (Llama, Falcon, Mistral, GPT-J, etc.). - Design and implement RAG frameworks for enterprise use cases combining vector databases, embeddings, and document stores. - Leverage Vertex AI for model training, fine-tuning, monitoring, and lifecycle management. - Build scalable AI pipelines and inference architectures using Python, TensorFlow, PyTorch, and LangChain frameworks. - Develop prompt engineering and optimization strategies for model reliability and contextual accuracy. - Collaborate with data engineers to design data pipelines for model training and evaluation. - Integrate AI models with enterprise systems via APIs and microservices architectures. - Define AI governance, ethical AI practices, and model performance KPIs. - Mentor cross-functional teams in AI/ML model development, deployment, and MLOps practices. - Evaluate new technologies, foundation models, and research to enhance AI platform capabilities. Required Skills & Experience: - 1215 years of overall experience in software/AI engineering, with at least 5+ years in AI architecture and applied ML. - Proven expertise in LLM development, fine-tuning, and RAG implementation using open-source frameworks. - Strong experience with Google Vertex AI (Model Registry, Pipelines, Workbench, and Model Deployment). - Proficiency in Python, TensorFlow, PyTorch, LangChain, Hugging Face Transformers. - Hands-on experience with Vector Databases (Pinecone, Weaviate, Milvus, pgvector, FAISS). - Familiarity with retrieval, embeddings (OpenAI, Vertex, Cohere, Hugging Face), and knowledge graphs. - Deep understanding of MLOps pipelines, CI/CD for AI, and cloud-based ML lifecycle management. - Experience integrating models with APIs, RESTful services, and microservices architectures. - Strong grounding in AI model governance, bias detection, and ethical AI frameworks. Preferred Qualifications: - Experience with multi-cloud AI architectures (AWS Sagemaker, Azure ML, GCP Vertex AI). - Familiarity with GenAI orchestration frameworks (LangChain, LlamaIndex, DSPy). - Contributions to open-source AI/ML repositories or model development communities. - Certifications in AI/ML Engineering, Cloud AI Architecture (GCP Professional ML Engineer). - Exposure to RAG-based enterprise chatbots or domain-specific LLM deployments. As an AI Architect at our company, your role will involve designing, building, and deploying enterprise-grade AI solutions with expertise in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Vertex AI platform. You will be responsible for architecting LLM-based solutions, designing RAG frameworks, leveraging Vertex AI, building scalable AI pipelines, and integrating AI models with enterprise systems. Additionally, you will mentor cross-functional teams, define AI governance practices, and evaluate new technologies to enhance our AI platform capabilities. Key Responsibilities: - Architect and lead the development of LLM-based solutions using open-source and proprietary models (Llama, Falcon, Mistral, GPT-J, etc.). - Design and implement RAG frameworks for enterprise use cases combining vector databases, embeddings, and document stores. - Leverage Vertex AI for model training, fine-tuning, monitoring, and lifecycle management. - Build scalable AI pipelines and inference architectures using Python, TensorFlow, PyTorch, and LangChain frameworks. - Develop prompt engineering and optimization strategies for model reliability and contextual accuracy. - Collaborate with data engineers to design data pipelines for model training and evaluation. - Integrate AI models with enterprise systems via APIs and microservices architectures. - Define AI governance, ethical AI practices, and model performance KPIs. - Mentor cross-functional teams in AI/ML model development, deployment, and MLOps practices. - Evaluate new technologies, foundation models, and research to enhance AI platform capabilities. Required Skills & Experience: - 1215 years of overall experience in software/AI engineering, with at least 5+ years in AI architecture and applied ML. - Proven expertise in LLM development, fine-tuning, and RAG implementation using open-source frameworks. - Strong experience with Google Vertex AI (Model Registry, Pipelines, Workbench, and Model Deployment). - Pr