AI ML Architect

3 days ago

Bengaluru, Karnataka, India Capgemini Full time ₹ 20,00,000 - ₹ 25,00,000 per year
Job Description
  • 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
Job Description - Grade Specific
  • 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, , 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

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