Job Description

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Define and own the end-to-end architecture for enterprise-scale GenAI/AI solutions Design reference architectures, reusable patterns, and best practices for integrating GenAI into business applications Collaborate with domain leaders, data scientists, security and developers to align business requirements with scalable AI architectures Select and evaluate LLMs, vector databases, and orchestration frameworks based on performance, compliance, and cost trade-offs Architect RAG pipelines, agentic workflows, and multi-agent ecosystems for production-grade deployments Ensure security, privacy, and governance frameworks are embedded in AI systems from inception Drive adoption of cloud-native AI services (Azure OpenAI, AWS Bedrock) and optimize for scalability and performance Guide teams in model lifecycle management, including deployment, monitoring, retraining, and drift handling (MLOps) Evaluate and recommend tools, frameworks, and protocols (e.g., MCP, LangChain, LangGraph) for robust interoperability Stay ahead of the curve on GenAI/AI advancements, regulations, and enterprise adoption trends, and translate them into actionable roadmaps

- Grade Specific

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Bachelor's/master's degree in computer science, Statistics, Engineering, or related field Proven experience as an AI/ML/GenAI Architect designing large-scale AI/ML systems Deep expertise in Python ecosystem, ML/DL frameworks (PyTorch, TensorFlow etc) Strong knowledge of LLM architectures, fine-tuning techniques (LoRA, PEFT, adapters), and deployment strategies Expertise in RAG pipelines, embeddings, and vector databases (Elastic, Pinecone, Milvus etc.) Familiarity with agentic GenAI systems (LangChain, LlamaIndex, AutoGen, Crew.ai, LangGraph) and Model Context Protocol (MCP) Experience in cloud-native architecture (AWS, Azure) and container orchestration (Docker/Kubernetes) Solid understanding of MLOps principles: CI/CD for ML, observability, retraining pipelines, and model governance Strong ability to bridge business and technology, communicating complex AI strategies to stakeholders Deep understanding of Responsible AI principles and ability to embed governance, compliance, and ethical frameworks into GenAI solution design. * Bonus: Experience in enterprise-scale AI adoption across Telecom industries