Strong hands-on experience in Generative AI, Agentic AI and Large Language Model (LLM) implementations.
Proven experience with Google Gemini and open-source LLM ecosystems.
Advanced Python development skills and experience building enterprise-grade applications.
Strong experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
Understanding of Agent Development Kits (ADK), Model Context Protocol (MCP) and AI orchestration frameworks.
Experience developing REST APIs, microservices and distributed applications.
Hands-on experience with Google Cloud Platform (GCP) and cloud-native architectures.
Strong understanding of MLOps, CI/CD pipelines, model lifecycle management and deployment automation.
Experience with Docker, Kubernetes and container orchestration.
Excellent problem-solving, communication and stakeholder management skills, with the ability to translate business requirements into technical solutions.
Desirable ExperienceExperience within banking, financial services, insurance or other regulated industries.
Experience with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI or Semantic Kernel.
Familiarity with vector databases such as Pinecone, Weaviate, ChromaDB or pgVector.
Knowledge of AI governance, Responsible AI, compliance and model risk management.
Experience with prompt engineering, AI guardrails, LLM security and evaluation techniques.
Experience implementing observability and monitoring for AI platforms and production workloads.
Understanding of event-driven architectures, messaging platforms and enterprise integration patterns.
Exposure to data engineering, enterprise data platforms and feature stores.
Experience leading technical design discussions and collaborating across multidisciplinary engineering teams.
Relevant Google Cloud, Generative AI, machine learning or Kubernetes certifications.
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