The role involves modernizing legacy data systems by refactoring backend logic into Python and optimizing complex T-SQL queries. Additionally, the engineer will develop robust APIs, build intuitive frontend dashboards with ReactJS, and manage application deployments using Kubernetes.
Develop and deploy Model Context Protocol (MCP) integrations to connect critical data sources like Snowflake and SharePoint for AI workflows. Refactor proof-of-concept code into robust production infrastructure while optimizing prompts for AI agents.
Design, build, and evolve scalable full-stack applications, including dynamic React and TypeScript interfaces, robust Node.js services, and efficient database solutions. Manage AWS infrastructure and CI/CD, apply AI-assisted and specification-driven development practices, ensure code quality and security through testing and reviews, and collaborate with business stakeholders in an Agile environment.
Design, build, and maintain scalable data pipelines and infrastructure for batch and streaming data ingestion. Collaborate with Data Scientists and ML Engineers to automate MLOps workflows and ensure data quality across cloud platforms.
Design, build, and maintain scalable AWS data pipelines and optimized ETL processes using PySpark and SQL, while collaborating with stakeholders to deliver reliable data solutions. Monitor and tune pipeline performance, ensure data quality and governance, document workflows, and resolve second-level incidents.
Design and build intelligent conversational agents and automated workflows using Python and the Microsoft AI stack. Manage end-to-end cloud infrastructure, enterprise data integrations, and observability strategies to ensure high-performance AI solutions.
You will lead the end-to-end development of generative and multimodal AI solutions, integrating them into production environments. Additionally, you will participate in model fine-tuning, testing, and ensuring system resilience through robust observability and quality controls.
You will act as a facilitator between business needs and infrastructure teams to design and implement scalable cloud solutions. Responsibilities include managing infrastructure requirements, participating in modernization initiatives, and overseeing automation and observability processes.
Design and implement scalable machine learning architectures on AWS, focusing on SageMaker and Amazon Neptune. Define guidelines for model development, training, deployment, and monitoring while ensuring security and operational excellence.
Lead and mentor a junior team in the development, training, and deployment of machine learning models on AWS. Define MLOps best practices, monitor model performance, and ensure reliability and explainability in production environments.
Design and maintain high-throughput batch and streaming data pipelines to process global financial transactions. Collaborate with cross-functional teams to build scalable data warehouse and lakehouse architectures while enforcing data quality and governance standards.
Lead the greenfield implementation of Microsoft Intune, Entra ID, and Microsoft Defender for Endpoint across a multi-cloud environment. Define device management policies, configure endpoint security, and facilitate knowledge transfer to internal engineering teams.
Lead the end-to-end migration of a native Android application to a unified React Native ecosystem for industrial plant operators. Ensure critical offline-first capabilities and resilient integration with AWS IoT and Cloud services.
