Oowlish

• Analyze customer behavior, product performance, and business trends using large datasets. • Build dashboards, reports, and visualizations that support business decision-making. • Develop data models and queries to extract meaningful insights from multiple data sources. • Identify patterns, trends, and opportunities using statistical analysis and exploratory data techniques. • Collaborate with Product, Marketing, Engineering, and Business teams to support strategic initiatives. • Translate complex analyses into clear, actionable recommendations for stakeholders. • Support forecasting, segmentation, and customer behavior analysis. • Contribute to long-term analytics projects from planning through execution. • Continuously improve reporting processes, data quality, and analytics methodologies.

• Collaborate with premier clients from the United States and Europe. • Contribute to pioneering digital solutions. • Build robust applications to enhance user experience. • Work on projects utilizing REST APIs, PostgreSQL, Elasticsearch, and AWS.

• Design and implement Retrieval-Augmented Generation (RAG) architectures. • Build AI-powered chatbot experiences using AWS Bedrock. • Develop multi-source data ingestion and retrieval pipelines. • Design and optimize embeddings and semantic search strategies. • Build scalable vector search solutions for enterprise knowledge retrieval. • Integrate structured and unstructured data sources into AI workflows. • Develop prompt engineering strategies to improve response quality and accuracy. • Build cloud-native AI services using AWS. • Collaborate with engineering teams to improve AI performance, scalability, and reliability. • Continuously evaluate new AI technologies and best practices for enterprise LLM applications.

• Design and develop enterprise AI solutions using AWS Bedrock. • Build AI-powered workflow automation for complex business processes. • Develop AI agents using AWS Bedrock Agents and Guardrails. • Integrate enterprise platforms, including ServiceNow and SAP. • Develop secure REST API integrations across enterprise applications. • Build Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge bases. • Develop document-processing workflows, including PDF extraction and business-rule validation. • Design cloud-native backend services using AWS. • Build scalable React interfaces for AI-powered business applications. • Develop GenAI CI/CD pipelines and automation workflows. • Implement Responsible AI principles and governance across AI solutions. • Collaborate with engineering and business teams to continuously improve AI capabilities.

• Design and develop AI-powered automation solutions using AWS Bedrock. • Build and orchestrate AI agents capable of handling IT help desk workflows. • Develop integrations with enterprise platforms using REST APIs. • Implement automation for ticket triage, routing, and access request workflows. • Build serverless applications using AWS Lambda and Step Functions. • Integrate AI workflows with KACE, Okta, Azure AD, and similar enterprise platforms. • Design secure, scalable, and maintainable cloud-native solutions. • Collaborate with engineering teams to continuously improve AI agent performance. • Participate in architecture discussions and technical decision-making.

• Design and develop modern web applications using React and Python. • Build intuitive chat-based user interfaces for AI-powered applications. • Integrate frontend applications with AWS Bedrock AI services through REST APIs. • Develop scalable backend services supporting web applications. • Build secure authentication and user session management. • Optimize application performance, responsiveness, and usability. • Collaborate closely with AI engineers to integrate conversational AI capabilities. • Build reusable UI components and maintain clean application architecture. • Participate in technical design discussions and code reviews. • Ensure high-quality, maintainable, and scalable code.

• Own the reliability, availability, and operational excellence of business-critical production systems. • Define how reliability is measured. • Lead incident response during production outages. • Drive observability strategy. • Continuously improve operational practices across high-availability environments. • Managing SLOs and leading major incidents.

• Define and drive product initiatives from discovery through delivery. • Build and improve product management processes where structure does not yet exist. • Collaborate closely with engineering teams, leadership, customers, and business stakeholders. • Translate customer feedback, business objectives, and technical constraints into actionable product requirements. • Drive product discovery, validation, roadmap planning, and feature prioritization. • Monitor feature adoption, user behavior, and product performance after launch. • Use analytics, experimentation, and customer insights to guide product decisions. • Facilitate communication between engineering teams, leadership, and customers. • Influence cross-functional teams without direct management responsibility. • Leverage modern AI tools to improve research, documentation, analysis, and overall product workflows.

• Design, develop, and maintain applications built with Grails and related technologies. • Build scalable and reliable backend services and APIs. • Work with AWS services to support application deployment, performance, and scalability. • Collaborate with engineering teams to design and implement technical solutions. • Troubleshoot, debug, and optimize existing applications. • Participate in code reviews and technical discussions. • Contribute to software architecture and technical decision-making. • Ensure software quality, reliability, and maintainability.

• Design, implement, and improve Site Reliability Engineering practices across production environments. • Define, manage, and continuously improve Service Level Objectives (SLOs), Service Level Indicators (SLIs), and Error Budgets. • Lead and participate in incident response and incident command processes. • Build and evolve observability strategies, including monitoring, logging, alerting, and distributed tracing. • Improve system reliability, availability, scalability, and operational efficiency. • Partner with engineering teams to improve application performance and production readiness. • Develop automation solutions that reduce operational overhead and improve reliability. • Participate in root cause analysis and post-incident reviews. • Drive continuous improvement initiatives based on operational insights and incident learnings. • Help establish reliability best practices across teams and services.