Applications Development
FinTech
ApplicationsDevelopmentSrProgrammerAnalyst-C12
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“Applications Development Sr Programmer Analyst - C12 at Applications Development”
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Full Job Description
**Job Summary** We are seeking a highly motivated and experienced Principal Engineer to join our Retail and Wealth Risk Engineering team under the Enterprise Risk Technology platform. This is an intermediate-level position responsible for designing, building, and maintaining robust, scalable data pipelines and solutions that leverage cutting-edge Mandatory platform for the secure and scalable deployment of AI agents, Big Data, Databrick and AI technologies. The ideal candidate is a high-impact individual with a passion for data, analytics, and problem-solving. You will play a key role in driving business engagement and growth by building the next generation of data and analytics platforms. ## Responsibilities * Design, develop, and maintain scalable, enterprise-grade AI agents , supporting ELT/ETL processes to handle large data volumes using the Python, FAST API, Microservices , PySpark, Kafka and Databricks ecosystem. * Build and Deploy GEN AI Agents using **Googles ADK and Google Flash 2.5 + LLMs to support application automation supports and its deep insights, workflow support with HIL - Human in loop architecture.** * Build and maintain data federation layers for lambda and Data Mesh architectures using tools like **Starburst, with a strategy for adopting AI-based use cases (e.g., machine learning, deep learning, NLP) to drive efficiency.** * Develop, deploy, and automate microservice integrations to support data-intensive applications, ensuring scalability, resilience, and maintainability **using cloud native infrastructure and openshift or Kubernates architecture including CI/CD pipelines.** * Integrate and leverage agentic AI tools (e.g.**, Devin.AI, Github Copilot)** and platforms (e.g.,**MCP**) through advanced prompt engineering to enhance development and operational efficiency. * Ensure data quality, integrity, and security throughout the entire data lifecycle. * Contribute to the continuous improvement of data engineering processes, standards, and best p
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