Jobs at EXL

Lead account management support, presales, and solutioning for healthcare provider clients. Drive account research, pipeline governance, RFP/RFI orchestration, SFDC reporting, business reviews, and cross-functional coordination to expand revenue and ensure client satisfaction.

Lead, train, and mentor staff to ensure high engagement and productivity. Oversee client service standards, handle escalated client issues, and resolve problems promptly to maintain satisfaction and performance.

Design, build, and maintain scalable cloud-native data pipelines and dimensional data models using DBT, SQL, and AWS services. Implement infrastructure-as-code with AWS CDK, automate CI/CD, ensure data quality and governance, optimize performance, and collaborate with cross-functional teams and offshore partners to deliver analytics-ready data assets.

Design, build, maintain, and execute automated UI and API tests using Selenium, TestComplete, ReadyAPI/Postman/Playwright. Create automation frameworks from scratch, integrate tests with CI/CD pipelines and Jenkins, and connect automation with test management tools (JIRA/DevOps). Work within Agile/SDLC processes, collaborate with cross-functional teams, and ensure test reliability and quality for web applications.

Lead end-to-end Genesys Cloud CX implementations delivering omnichannel contact center solutions. Design IVR/ACD/Architect flows, routing, QM, WFM, agent scripting, telephony (SIP/SBC), bot integrations (Dialogflow), CRM and API integrations, and data pipelines to Snowflake for reporting and analytics. Oversee client design, build teams, and delivery.

Define technical vision and lead end-to-end development of Google CCAI initiatives. Design generative, agentic conversational architectures (Dialogflow CX), implement Python-based webhooks and serverless backends on GCP, build tool/function-calling integrations, manage Protos and data payloads, create automated evals and CI/CD for voice agents, and optimize prompt/context for LLM-driven agent experiences.

Build and maintain Python-based Generative AI applications and RAG pipelines. Implement and optimize LLMs (GPT, LLaMA, Mistral, FLAN T5), embeddings, indexing, chunking, reranking, prompts, and evaluation. Integrate multiple data sources, use cloud GPU resources (AWS/Azure/Databricks), design agentic/multi-agent systems with LangGraph/CrewAI/AutoGen, collaborate across teams, document specifications, and assist in delivering client solutions.

Perform non-clinical case research and eligibility verification, sort and prioritize cases, validate and document data for stateside Data Administration, liaise with dependent teams, and ensure regulatory, accreditation, and confidentiality standards are met.

Lead analytics initiatives: analyze large datasets, build and maintain Power BI/Tableau dashboards and KPI scorecards, collaborate with stakeholders, ensure data quality, deliver ad-hoc analyses, manage multiple projects, mentor team members, and present insights to leadership to support strategic decision-making.

Design, develop, and deploy GenAI applications using LLMs, RAG, and orchestration frameworks. Build prompt-based solutions, vector DB integrations, document ingestion and retrieval pipelines. Create scalable APIs and microservices, design testing strategies and synthetic datasets, and collaborate with AI architects, data engineers, and product stakeholders to deliver reliable, performant AI-powered business features.

Design, build, and optimize scalable AI-ready data pipelines on GCP and Snowflake. Ingest, transform, validate, and integrate structured data, create synthetic datasets, build APIs and backend workflows, and implement monitoring, CI/CD, and cost/performance optimizations to support AI and analytics solutions.

Lead design, development, and deployment of enterprise Generative AI solutions: architect LLM-based systems, build orchestration pipelines, establish prompt governance and guardrails, implement RAG and vector search, optimize inference and benchmarking, and convert prototypes into production across AWS, GCP, and Snowflake.

Work with data stewards and business stakeholders to define Critical Data Elements, build data dictionaries, and map business terms to the enterprise glossary. Support Data Quality by linking business and quality rules to technical metadata. Build reporting infrastructure, dashboards, alert automations, visualizations, and polished presentations using Tableau and Advanced SQL.

Perform general PC-based tasks including strong Microsoft Excel use, web research, and navigation. Require 1+ years insurance process experience for Level 3, typing speed of 40 WPM at 98% accuracy, and a graduate-level education (15 years).

Lead creative direction, brand voice, and visual design for GTM materials. Produce client pitches, thought leadership, and partner co-branded content. Support sales pursuits with pipeline visibility in Salesforce, manage external creative vendors, and set the organizations standard for AI-first product presentation.

Perform regular and ad-hoc reconciliations of balance sheet accounts (cash, AR, AP, etc.), investigate and resolve discrepancies between ledgers and external statements, and maintain accurate financial records to support reporting and compliance.

Execute end-to-end Record to Report (R2R) tasks including general ledger accounting, journal entries, balance sheet and bank reconciliations, fixed asset and lease accounting, month-end/year-end close support, and preparation of variance analysis and financial close schedules.

Design and deploy GenAI and agentic AI solutions to automate business workflows (claims, underwriting, customer service). Analyze structured and unstructured data, build production-grade LLM-based systems, lead client sessions, identify opportunities, and guide delivery teams while maintaining stakeholder relationships.

Lead and oversee solution architecture across the organization, drive high-impact architecture initiatives, ensure architectural practices align with business goals, and provide executive-level strategic guidance.

Design, develop, and deploy data-driven solutions across the retail lending lifecycle. Manage and analyze multiple data sources, build ML models, resolve data issues with client IT, and present technical findings clearly to stakeholders.