AI Architect

- Experience in architecting and solutioning in Gen AI, Agentic AI, classic ML, and automation space - Good understanding of Prompt engineering, RAG pipelines, Supervised/unsupervised Model tuning, MLOps/LLMOps pipelines, and AI observability - Experience in Enterprise-grade RAG-based solutions with LLMs (OpenAI, Hugging Face, LLaMA, etc.) and vector databases (Pinecone, Weaviate, FAISS, etc.) - Ability to design enterprise-level AI/Gen AI platform/solutions with the clients existing enterprise stack - Hands-on mastery of core GenAI frameworks (e.g., LangChain, LlamaIndex) and practical experience with Agentic AI frameworks (e.g., AutoGen, CrewAI, LangGraph) - Strong working knowledge and practical deployment experience on at least one major cloud platform (AWS, Azure, GCP), including their AI/ML services - Strong foundation in software engineering principles for building scalable, maintainable, and production-ready AI systems - Proficiency in Python with AI/ML frameworks (PyTorch, TensorFlow) - Experience with MLOps/LLMOps tools (MLflow, Kubeflow, Docker, Kubernetes) - Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., Hugging Face Transformers, spaCy, NLTK) - Proven experience developing applications leveraging state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude) including prompt engineering, fine-tuning, and evaluation - Strong grasp of advanced techniques such as complex task decomposition for agents, reasoning engines, knowledge graphs, and evaluation methodologies for complex AI systems - Strong articulation, stakeholder management, and consulting skills - Experience working on client proposals RFI/RFP/Orals stages - Experience leading a team of AI/software engineers