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AI Engineer | Bank


Key Responsibilities

  • Design, develop, and maintain backend services, APIs, and AI-powered applications using Python
  • Build and implement AI agents (e.g., copilots, automation agents, and intelligent assistants) leveraging LLMs and agent frameworks
  • Develop Generative AI solutions, including:
    • Chatbots and conversational systems
    • Retrieval-Augmented Generation (RAG) applications
    • Knowledge assistants and document intelligence solutions
  • Design and manage agent orchestration, tool integration, and multi-step reasoning workflows
  • Integrate AI solutions with internal banking systems and external services
  • Support deployment and operation of AI services in containerized environments (Docker, Kubernetes, OpenShift)
  • Apply DevOps / MLOps / LLMOps practices for continuous integration, deployment, and monitoring
  • Develop and maintain frontend components for internal AI services where required
  • Collaborate closely with cross-functional teams and vendors, providing technical guidance and best practices
  • Ensure AI solutions meet security, compliance, and regulatory standards in the banking industry

Required Qualifications

  • Bachelor's degree or above in Computer Science, Data Science, Engineering, or related disciplines
  • Minimum 3-5 years of experience in software engineering, AI engineering, or related roles
  • Strong proficiency in Python programming
  • Hands-on experience with Generative AI / LLMs, including prompt engineering
  • Experience building or working with AI agents or agent frameworks (e.g., LangChain, Semantic Kernel, AutoGen, CrewAI)
  • Understanding of RAG architectures, embeddings, and vector databases
  • Experience with backend development, APIs, and system integration
  • Hands-on experience with containerization and orchestration (Docker, Kubernetes, OpenShift)

Nice to Have

  • Frontend development experience (e.g., React, Next.js)
  • Experience in banking or financial services environment
  • Understanding of the Generative AI ecosystem, including:
    • Agentic architectures
    • RAG (Retrieval-Augmented Generation)
    • MCP (Model Context Protocol) or similar frameworks
  • Experience with Google cloud platform GCP and vector databases