Source description
About the role
Define end-to-end architecture for Generative AI solutions, including LLMs, RAG frameworks, and Agentic AI systems.
Lead architectural decisions for scalable, secure, and cost-optimized Generative AI platforms.
Design and implement RAG pipelines, vector databases, and knowledge retrieval systems.
Evaluate, select, and integrate LLMs such as OpenAI, Anthropic, and open-source models.
Guide teams on prompt engineering, fine-tuning, model evaluation, and optimization.
Collaborate with business stakeholders to translate use cases into technical architectures.
Establish best practices for AI governance, security, privacy, and compliance.
Mentor AI/ML engineers and data scientists, and review technical designs and code.
Drive Generative AI adoption across enterprise applications and platforms.
10+ years of experience in AI/ML, Data Science, or Advanced Analytics.
Strong hands-on and architectural experience with Generative AI and LLM-based systems.
Expertise in RAG architectures, LangChain, LangGraph, and agentic frameworks.
Proficiency in Python and AI/ML frameworks such as PyTorch, TensorFlow, and Hugging Face.
Experience working with vector databases including Pinecone, FAISS, Weaviate, and Chroma.
Strong cloud experience across AWS, GCP, or Azure with AI/ML services.
Experience designing microservices and API-based architectures.
Excellent communication skills with strong stakeholder management capabilities.
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