AI ML Architect

1 day ago

Bengaluru, Karnataka, India Capgemini Full time ₹ 20,00,000 - ₹ 25,00,000 per year

*AI ML Architect*

Location: Bangalore

Experience: 8+ Years

Your Role

As a AI ML Architect, you will own the end-to-end architecture of enterprise-scale AI systems. Youll collaborate across domains to design reference architectures, evaluate technologies, and guide implementation of production-grade GenAI solutions.

In this role, you will:

  • 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 experts, 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 GenAI/AI advancements, regulations, and enterprise adoption trends, and translate them into actionable roadmaps

Your Profile

  • Bachelors or Masters degree in Computer Science, Data Science, or related field
  • 8+ years of experience in AI/ML architecture and enterprise solution delivery
  • Proven expertise in designing scalable GenAI systems and integrating LLMs into business applications
  • Strong understanding of RAG pipelines, agentic workflows, and multi-agent orchestration
  • Hands-on experience with GenAI frameworks (LangChain, LangGraph, MCP) and vector databases
  • Familiarity with cloud-native AI platforms (Azure OpenAI, AWS Bedrock) and MLOps practices
  • Deep knowledge of security, privacy, and governance in AI systems
  • Excellent collaboration and communication skills across technical and business teams

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