HRS Healthcare

• Lead end-to-end design and delivery of AI modules — from architecture to production deployment — for complex, multi-component features, leveraging GCP and Microsoft AI ecosystem capabilities • Define LLM integration patterns, RAG strategies, and data pipeline architectures; own technical quality and performance of these systems. • Act as the primary technical interface with cross-functional stakeholders; participate in requirements workshops and solution demos. • Drive design reviews, establish engineering standards, and actively mentor engineers on the team. • Identify and address technical debt, reliability risks, and scalability bottlenecks proactively. • Deliver agentic automation solutions across enterprise use cases

• Own end-to-end program governance — establishing the PMO structure, governance forums, and reporting cadences. Define and manage the integrated program plan across all AI workstreams (Generative AI, Data Science, GCP infrastructure, integrations), tracking milestones, dependencies, and critical path. • Run project planning, reviews, and RAG with technical leads; maintain velocity metrics and remove blockers. • Ensure all deliverables are tracked against scope, schedule, budget, and quality criteria, with proactive escalation of deviations. • Maintain program documentation — charter, WBS, RACI, status reports, and decision logs — to the highest standards. • Own the program RAID log. • Proactively identify, assess, and prioritise risks across all workstreams with clear ownership and mitigation plans. • Design and enforce the change control process — evaluating scope changes for impact on timeline, budget, and resources before presenting recommendations to the steering committee. Establish escalation pathways and manage critical issues to resolution. • Conduct regular risk workshops to embed a risk-aware culture within the delivery team. • Serve as the primary point of contact for CLIENT's program stakeholders — building trusted relationships with business sponsors, technology executives, and operational teams. • Prepare and present executive-level status reports, steering committee packs, and milestone reviews to senior leadership. • Facilitate alignment between CLIENT's internal teams and the delivery organisation, managing expectations and resolving conflicts. Conduct regular CSAT assessments and act on feedback to continuously improve the engagement model. • Own resource planning across the program — forecasting demand, managing onboarding/offboarding, and ensuring the right skills are available across all workstreams. • Track utilization, manage bench transitions, and coordinate with HR and recruitment for timely fulfilment of open roles. • Foster a high-performance, distributed team culture — supporting wellbeing, recognizing contributions, and maintaining morale. • Own the program budget — tracking actuals against forecast, managing contingency, and producing monthly financial reports for internal and client governance. • Identify and escalate budget risks early; implement corrective actions to maintain P&L health. • Support commercial activities including SOW management, change order negotiations, invoice tracking, and contract renewals. • Contribute effort estimates, resource plans, and delivery timelines to pre-sales and solutioning for new workstreams or extensions. • Maintain working knowledge of the GCP and Google AI stack — including Vertex AI, BigQuery, and associated data and ML services — to effectively govern delivery across infrastructure, model development, and integration workstreams. • Oversee delivery of intelligent automation and RPA workstreams — understanding bot lifecycle management, exception handling frameworks, orchestration platforms, and the interplay between RPA, AI, and enterprise systems. • Adapt PM practices to the non-linear, iterative nature of ML and AI delivery — managing experimentation phases, data dependency risks, model validation cycles, and the transition from prototype to production. • Act as a translator and bridge between business stakeholders who think in outcomes and technical teams who think in architectures — facilitating shared understanding and aligned decision-making across both worlds. • Establish and track program KPIs and OKRs; produce regular insights on delivery health, team performance, and program ROI. • Lead retrospectives and implement lessons learned to build a culture of continuous improvement. • Contribute to internal PM frameworks, playbooks, and templates for AI program delivery — creating reusable assets for future engagements. • Represent the organisation in client QBRs, program audits, and governance reviews, maintaining the program's compliance posture at all times.

• Ensure prompt ticket follow up and action for L2 issues, ensuring active work toward resolution while time counting and prompt response to stop clock when need input or action from client; ensure clear info in all tickets and knowledge base update after ticket closure. • Use your data analytics skills to analyze the automated business processes of our clients and identify and eliminate potential “troublemakers” before they become problems • Technically extend, improve, and customize automation solutions while maintaining close and regular contact with our clients • Collaborate with L1 support and development teams to address complex technical challenges. • Conduct routine maintenance checks on existing automations to ensure optimal performance. • Coordinate operations and maintenance activities for large client accounts • Test and optimize the software design, code, and reusability of our automation artifacts • Apply and further develop best practices for developing automation solutions • Provide clear and concise communication to end-users and stakeholders regarding issue resolutions and maintenance schedules • Consult, provide expertise, guidance, and participation during the Onboarding and Service Transition of clients

• Own the full automation lifecycle, from discovery and requirements gathering through solution design, development, testing, and production deployment. • Lead net new automation initiatives independently or contribute as a key member of larger delivery programs. • Work closely with business stakeholders, delivery pods, subject matter experts, and client technology teams, acting as the primary point of contact for requirement clarity, solution intent, and delivery alignment. • Support or lead platform migration initiatives (e.g., Blue Prism to UiPath), ensuring solutions are optimized for the target platform rather than simply lifted and shifted. • Gain exposure to Agentic AI, Intelligent Document Processing (IDP), Test Automation, and advanced automation initiatives, contributing to the continuous evolution of automation offerings. • Act as the primary liaison between business stakeholders and technical delivery teams, ensuring clear communication and alignment throughout the automation lifecycle. • Lead process discovery sessions, workshops, and stakeholder interviews to elicit, challenge, and validate business requirements. • Design, build, test, and deploy UiPath automations following established best practices and delivery standards.

• Design, develop, and maintain interactive Power BI dashboards for RPA operations, delivery metrics, and business KPIs. • Build reports tracking UiPath bot performance: automation success rates, exception handling, queue throughput, and SLA adherence. • Create consolidated views integrating data from multiple sources into a single pane of glass for stakeholders. • Translate business requirements from operations and delivery leads into intuitive, actionable visualizations. • Extract data from UiPath Orchestrator APIs, Insights module, and Elasticsearch logs. • Integrate data from Jira (sprint metrics, backlog, defects, velocity) and Azure DevOps (work items, pipelines, releases). • Connect additional data sources including Excel, SQL Server, REST APIs, SharePoint as required. • Build and maintain Power Query/M transformations and robust data models with proper relationships and hierarchies. • Develop and optimize DAX measures and calculated columns for complex KPI logic. • Define and implement standard KPIs: automation ROI, FTE savings, bot uptime, error rate, process cycle time, and delivery velocity. • Work with RPA CoE, project managers, and business analysts to align on metric definitions and reporting cadence. • Set up automated data refresh schedules and alerts via Power BI Service/Power BI Gateway. • Maintain data accuracy, lineage, and documentation for all reports and datasets. • Implement Row-Level Security (RLS) for role-based access to sensitive dashboards. • Conduct peer reviews and enforce best practices for data modeling, naming conventions, and report design. • Ensure Power BI workspace governance including dataset certifications and workspace management. • Partner with RPA developers, project managers, and delivery leads to understand data requirements. • Present dashboards and insights to senior leadership and client stakeholders when required. • Conduct training sessions and create documentation for dashboard users.

• Own end-to-end program governance — establishing the PMO structure, governance forums, and reporting cadences. • Define and manage the integrated program plan across all AI workstreams (Generative AI, Data Science, GCP infrastructure, integrations), tracking milestones, dependencies, and critical path. • Run project planning, reviews, and RAG with technical leads; maintain velocity metrics and remove blockers. • Ensure all deliverables are tracked against scope, schedule, budget, and quality criteria, with proactive escalation of deviations. • Maintain program documentation — charter, WBS, RACI, status reports, and decision logs — to the highest standards. • Own the program RAID log. • Proactively identify, assess, and prioritize risks across all workstreams with clear ownership and mitigation plans. • Design and enforce the change control process — evaluating scope changes for impact on timeline, budget, and resources before presenting recommendations to the steering committee. • Establish escalation pathways and manage critical issues to resolution. • Conduct regular risk workshops to embed a risk-aware culture within the delivery team. • Serve as the primary point of contact for client program stakeholders — building trusted relationships with business sponsors, technology executives, and operational teams. • Prepare and present executive-level status reports, steering committee packs, and milestone reviews to senior leadership. • Facilitate alignment between client's internal teams and the delivery organization, managing expectations and resolving conflicts. • Own resource planning across the program — forecasting demand, managing onboarding/offboarding, and ensuring the right skills are available across all workstreams. • Track utilization, manage bench transitions, and coordinate with HR and recruitment for timely fulfillment of open roles. • Foster a high-performance, distributed team culture — supporting well-being, recognizing contributions, and maintaining morale. • Own the program budget — tracking actuals against forecast, managing contingency, and producing monthly financial reports for internal and client governance. • Identify and escalate budget risks early; implement corrective actions to maintain P&L health. • Support commercial activities including SOW management, change order negotiations, invoice tracking, and contract renewals. • Contribute effort estimates, resource plans, and delivery timelines to presales and solutioning for new workstreams or extensions. • Maintain working knowledge of the GCP and Google AI stack — including Vertex AI, BigQuery, and associated data and ML services — to effectively govern delivery across infrastructure, model development, and integration workstreams. • Understand the unique delivery dynamics of agentic AI programs — including multi-agent orchestration, tool-use design, prompt engineering cycles, and the governance considerations around autonomous decision-making systems. • Oversee delivery of intelligent automation and RPA workstreams — understanding bot lifecycle management, exception handling frameworks, orchestration platforms, and the interplay between RPA, AI, and enterprise systems. • Adapt PM practices to the non-linear, iterative nature of ML and AI delivery — managing experimentation phases, data dependency risks, model validation cycles, and the transition from prototype to production. • Act as a translator and bridge between business stakeholders who think in outcomes and technical teams who think in architectures — facilitating shared understanding and aligned decision-making across both worlds. • Establish and track program KPIs and OKRs; • Produce regular insights on delivery health, team performance, and program ROI. • Lead retrospectives and implement lessons learned to build a culture of continuous improvement. • Contribute to internal PM frameworks, playbooks, and templates for AI program delivery — creating reusable assets for future engagements. • Represent the organization in client QBRs, program audits, and governance reviews, maintaining the program's compliance posture at all times.

• Design, build, and deploy generative AI applications using Google Gemini (Pro, Ultra, Flash), PaLM 2, and other Google-hosted foundation models via Vertex AI. • Implement Retrieval-Augmented Generation (RAG) architectures using Vertex AI Search, Vector Search, and document embedding pipelines for enterprise knowledge retrieval. • Develop multi-modal AI capabilities leveraging Gemini's vision, text, and code understanding for hospitality use cases such as guest experience, analytics, and operations. • Build and maintain agentic AI workflows and orchestration using LangChain, LlamaIndex, or Google Agent Builder — integrating tools, APIs, and enterprise data sources. • Optimize prompt engineering strategies, system instructions, and grounding mechanisms for production-grade LLM deployments. • Develop end-to-end ML pipelines from data ingestion and feature engineering through model training, evaluation, and production deployment on Vertex AI Pipelines / Kubeflow. • Apply advanced data science techniques — statistical modelling, time-series forecasting, recommendation systems, and anomaly detection — for hospitality and gaming analytics. • Build scalable data transformation and feature engineering workflows using BigQuery, Dataflow, and Pub/Sub. • Implement model monitoring, drift detection, and automated retraining strategies to ensure sustained model performance in production. • Leverage TensorFlow, JAX, or PyTorch for custom model development where pre-trained solutions are insufficient. • Architect and manage cloud-native AI infrastructure on GCP — including Vertex AI, BigQuery ML, Cloud Run, GKE, Cloud Functions, and Cloud Storage. • Design secure, scalable, and cost-optimized GCP environments aligned with enterprise compliance requirements and CLIENT's data governance standards. • Implement CI/CD pipelines for ML model serving using Cloud Build, Artifact Registry, and Vertex AI Model Registry. • Set up monitoring, observability, and alerting for AI/ML workloads using Cloud Monitoring, Cloud Logging, and custom dashboards in Looker. • Build agents with Google ADK, deploy with Cloud Run / Agent Engine with Vertex. • Design and implement conversational AI agents using Dialogflow CX and Agent Builder for guest-facing and internal automation use cases. • Partner with CLIENT's business and technology stakeholders to define AI use cases, prioritize the roadmap, and translate requirements into technical deliverables. • Champion responsible AI practices — model fairness, explainability, content safety, and data privacy — across all AI solution designs. • Produce and maintain technical documentation including architecture decision records (ADRs), API specs, model cards, and runbooks. • Mentor junior engineers and lead knowledge-sharing sessions; contribute to AI community of practice within the delivery organization.

• Lead project workstreams, managing the project scope, budget, and timeline • Analyze data and information, and make recommendations to clients • Collaborate with other members of the automation team to deliver solutions that address client needs • Stay up to date on industry trends and best practices, and continuously improve consulting service offerings • Develop and maintain strong relationships with clients, serving as a trusted advisor to clients

• Act as a strategic automation advisor to enterprise clients • Conduct automation discovery workshops and capability assessments • Design end-to-end automation architectures • Lead and actively contribute to the development of agentic automation solutions • Build, configure, and deploy AI-powered agents • Develop and integrate Gemini-powered generative AI capabilities into automation pipelines • Communicate complex automation and AI concepts clearly to stakeholders • Manage technical delivery risk and maintain high client satisfaction

• Support project workstreams, contributing to the management of scope, timeline, and deliverables under senior guidance • Analyze data and information, and assist in preparing recommendations for clients • Collaborate with other members of the automation team to deliver solutions that address client needs • Stay up to date on industry trends and best practices, and proactively grow your consulting skills • Develop and maintain strong relationships with clients