Brillio

• Building state of the art pipelines and data models that are at the heart of the studio decision-making process. • Working on large-scale lakehouse and warehouse analytics systems that process data feeds in real-time and batch processes.

• Lead the architecture and design of enterprise Data & AI platforms supporting analytics, reporting, machine learning, and AI-driven business capabilities. • Define scalable data architecture, governance, security, and operational frameworks for modern cloud-native platforms. • Partner with business and technology stakeholders to identify and prioritize data modernization, AI, and automation opportunities. • Architect and guide implementation of AI, Generative AI, and Agentic AI solutions leveraging enterprise data assets. • Establish architecture standards, best practices, and technology roadmaps across data engineering and AI initiatives. • Provide technical leadership and mentorship to engineering, platform, and AI teams.

• Validate IVR call flows, routing logic, and customer journeys • Perform end-to-end testing of inbound, outbound, and callback flows • Test voice, chat, and self-service interactions for accuracy and usability • Test integrations between Amazon Connect, AWS services, and backend systems • Validate APIs, Lambda functions, and data flows across systems • Ensure correct handling of user inputs, fallback scenarios, and edge cases • Design detailed test cases, test scenarios, and test data • Execute functional, regression, system, and integration testing • Perform smoke and sanity testing during releases • Develop and maintain test automation scripts where applicable • Use tools like Cyara, Selenium, or API testing frameworks for validation • Identify, log, track, and retest defects • Work closely with developers and BA teams for issue resolution • Prepare test reports, status updates, and quality metrics • Ensure high-quality delivery aligned with business requirements • Validate compliance with security and data standards • Participate in UAT support and production validation

• Lead experimentation strategy: Design, execute, and analyze A/B tests and quasi-experiments to evaluate product and feature impact on engagement, retention, and customer satisfaction. • Drive product insights: Conduct deep-dive analyses on user journeys, onboarding funnels, feature adoption, and retention cohorts to identify growth and optimization opportunities. • Define and operationalize metrics: Establish north-star metrics, KPIs, and guardrails; ensure consistent definitions across teams and dashboards. • Enable product decision-making: Translate complex analyses into clear, actionable recommendations for product roadmaps and prioritization. • Improve data foundations: Partner with data engineering and platform teams to ensure high-quality telemetry, scalable data models, and reliable reporting layers. • Leverage advanced analytics: Apply statistical techniques (segmentation, cohort analysis, regression, causal inference) to uncover drivers of user behavior and product performance. • Collaborate cross-functionally: Work closely with Product Managers, Designers, and Engineers to embed analytics into the product development lifecycle. • Mentor and elevate analytics practices: Guide analysts on experimentation design, metric definition, and storytelling best practices. • Communicate effectively: Deliver clear, compelling insights to stakeholders and leadership through dashboards, presentations, and narratives.

• Lead end-to-end delivery of complex contact center transformation programs and strategic initiatives. • Manage multiple concurrent projects, ensuring delivery on scope, schedule, budget, and quality. • Collaborate with business leaders, technology teams, operations, and external partners to define project objectives and execution plans. • Drive program governance, including status reporting, risk management, issue resolution, and executive communications. • Lead cross-functional and geographically distributed teams to achieve project milestones. • Partner with contact center operations to improve customer experience, operational efficiency, and workforce performance. • Manage project budgets, resource planning, and vendor relationships. • Establish and maintain project management best practices, methodologies, and documentation. • Identify dependencies, proactively manage risks, and ensure timely escalation of critical issues. • Foster collaboration, accountability, and continuous improvement across project teams.

• Independently architect, scale, and own the E2E test strategy for complex web applications. • Build, maintain, and optimize robust automation frameworks utilizing Cypress and TypeScript/JavaScript to improve e2e coverage and automate production smoke testing. • Automate and embed production smoke tests and regression suites directly into our CI/CD pipelines to ensure rapid, high-confidence deployments. • Perform root-cause analysis on e2e failures, flaky tests, and pipeline bottlenecks by digging into Cypress and backend logs and interacting closely with software engineers. • Lead high-impact testing initiatives and author clear, actionable technical documentation.

• Design and implement robust statistical models and machine learning algorithms for large-scale data analysis and predictive analytics • Lead end-to-end development of data science projects, including hypothesis testing, regression analysis, classification, and forecasting • Collaborate with cross-functional teams to define business requirements, translate them into analytical solutions, and drive measurable impact • Optimize and automate data pipelines using Python, PySpark, and R, ensuring efficient data processing and feature engineering • Develop, validate, and maintain probabilistic graph models and advanced statistical computing frameworks • Utilize industry-leading ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn to build, train, and deploy models • Establish rigorous model evaluation and monitoring processes using tools like Great Expectations and Evidently AI • Mentor and guide junior data scientists, fostering technical excellence and continuous learning within the team

• Engage directly with business stakeholders, VPs, clinical leads, operations owners to surface ambiguous, high-value problems and translate them into buildable AI opportunities • Ask sharp questions, challenge assumptions, and define what a minimum viable AI solution looks like before writing a line of code • Map existing processes end-to-end, identify where AI can eliminate friction, reduce cost, or accelerate decisions and communicate your findings in business terms • Build lightweight scoping documents and ROI estimates that align stakeholders and set clear success criteria before development begins • Design and build working AI prototypes within days using Claude and the modern AI development stack • Develop full-stack AI applications spanning LLM orchestration, retrieval-augmented generation (RAG), agentic workflows, REST API integrations, and lightweight front-end interfaces • Own your technical architecture end-to-end from prompt design to data pipeline to user interface and be able to explain every design decision to both engineering and business audiences • Iterate based on real user feedback in the field ship early, learn fast, and drive toward production-grade quality with intention and speed • Partner with Brillio's AI Governance team to validate, harden, and evolve prototypes into production-grade, enterprise-ready systems. • Apply responsible AI principles bias review, explainability, HIPAA compliance, audit logging, and model monitoring as a standard part of every build. • Document solutions clearly and completely so they can be handed off, scaled, and maintained by broader engineering teams without rework. • Contribute to Brillio's reusable AI accelerator library turning client-specific solutions into patterns that can be deployed across engagements. • Participate in all client business rhythm meetings sprint reviews, program steering committees, and executive status updates • Demonstrate progress through live demos and working software not slide decks alone • Communicate technical concepts clearly to non-technical business leaders and build the trust required to move from pilot to scale • Surface blockers, dependencies, and risks early with clear, solutions-oriented framing • Develop genuine expertise in your client's business, their workflows, their data, their competitive pressures, and their regulatory environment • Understand healthcare operations deeply enough to generate your own problem hypotheses not just respond to inbound requests • Build relationships with business counterparts that go beyond project delivery, becoming a trusted advisor on what AI can and cannot do in their environment • Contribute to internal knowledge sharing bring learnings from client engagements back to Brillio's broader applied AI community

• Define and govern enterprise architecture frameworks, standards, and reference architectures • Develop technology roadmaps aligned to business transformation objectives • Drive architecture governance, design reviews, and compliance adherence across programs • Lead end-to-end cloud migration programs (AWS, Azure, GCP) from legacy/on-prem systems • Drive application modernization initiatives, including monolith to microservices transformation and cloud-native refactoring • Architect and enable SaaS platforms with multi-tenancy, scalability, and security • Provide technical leadership across full-stack engineering teams

• Consultant role at Brillio focusing on intelligent automation in claims management.