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 client’s existing enterprise stack. - Hands-on mastery of core GenAI frameworks (e.g., LangChain, LlamaIndex) and practical experience with Agentic AI frameworks and concepts (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, autonomous agent design, 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.