Consultant

• Analyze client data, develop and validate data hypotheses, identify and integrate relevant data sources, clean and structure data, and recommend actionable solutions that enhance efficiency and business performance.
• Conduct external research and market assessments for overseas project tasks, aligning with different teams on goals, expectations, and progress.

Senior Consultant – Analytics

Manager

• Report on development and automation using Power BI, Power Automate and Azure Synapse Analytics.
• Provide guidance and training to clients to use the reporting environment such as Power BI Service and Tableau Server.
• Interact with the client to get requirements and cascade the same across developers.
• Regularly connect with clients to walk them through the draft version of report/app and note any feedback or improvements required.
• Perform high level checks on the research completed by the team on new logics and obtain approval from client to work on the same.
• Ensure compliance with HIPAA/OIG at a process and data management level.
• Create and maintain appropriate documentation of compliance activities for the team.
• In case of compliance violation, ensure proper reporting of the same is done to the internal compliance team and the client as required.
• Position may work at various and unanticipated worksites throughout the United States. Telecommuting permitted.

Senior Manager

• Collaborate with client stakeholders to analyze and synthesize the client data to meet the business objectives, business intelligence dashboards, and descriptive/predictive/prescriptive analytics requirement.
• Plan project timelines, milestones, and implementation approaches in collaboration with client stakeholders providing timely updates.
• Establish best practices for project management (AGILE/JIRA/Kanban familiarity), team management, and documentation (confluence/slides development).
• Mentor project managers and other team members for consistent growth within the organization.
• Develop case studies, slides, and white papers highlighting work done by analytics engineering and data science teams.
• Participate in hiring drives and campus recruitment to strengthen and maintain the quality of analytics teams. Provide trainings for new hires to onboard them to a new tool and technology.
• Design data models and solutions for data pipelines for business intelligence, machine learning, reporting, and ad hoc analysis use cases.
• Determine how best to leverage machine learning and advanced analytic methods (ensemble models, decision trees and neural networks, operations research, and statistical modeling such as multivariate techniques) to support business objectives.
• Efficiently construct, develop, and implement the models in a variety of modeling tools, achieving highly accurate models.
• Contribute insights from conclusions of analysis that integrate with initial hypothesis and business objective. Communicate analyses by preparing presentations for and participating in C-level clients.
• Leverage best practices and industry leading tools and technologies to profile data, develop efficient ingestion, and build semantic data layers.
• Position may work at various and unanticipated worksites throughout the United States. Telecommuting permitted.

Engagement Manager

Data Analyst

• Develop and implement ETL (Extract, Transform, Load) processes in Azure Databricks to ingest and integrate new data sources into the data warehouse.
• Monitor and validate data flows to ensure accurate and timely data availability.
• Leverage Azure Databricks for data processing, transformation, and analysis tasks.
• Develop and optimize Spark jobs using SQL within Azure Databricks environment.

Healthcare Data Analyst

AI Engineer

• Define end-to-end architecture for agentic AI-enabled platform across data, AI, orchestration, and integration layers with some real hands-on experience doing POCs
• Design and govern agentic orchestration framework for multi-step production workflows
• Establish architecture patterns for - RAG and grounding, Vector search and retrieval, MCP tool access layer, prompt management and evaluation
• Have a deep understanding of Agentic coding and best practices of using Agentic coding for large scale implementations
• Familiarity in implementing A2A or similar frameworks in a large scale environment
• Define integration architecture across - Lakehouse, ODS, document systems, Underwriting systems and third-party APIs
• Design configurable, metadata-driven framework for multi-LOB onboarding
• Define API/microservices patterns (Python/.NET hybrid)
• Define where and how to use - GenAI vs deterministic logic, agentic workflows vs pipeline workflows
• Establish multimodal integration approach combining structured, unstructured, and external data
• Design prompt lifecycle, evaluation, and optimization strategy
• Define AI safety and guardrails (PII, hallucination control, policy constraints)
• Establish ModelOps and PromptOps frameworks
• Ensure explainability, auditability, and traceability of AI outputs
• Lead technical execution across AI, data, and platform teams
• Guide engineers (AI, data, full-stack) and ensure alignment with architecture
• Drive technical decisions and stakeholder communication

AI Architect

• Collaborate with data stewards and business stakeholders to analyze key Critical Data Elements and build data dictionaries.
• Guide stakeholders in identifying Critical Business Terms and mapping them to the enterprise business glossary and data flow documentation.
• Support the Data Quality Team in mapping business and data quality rules to relevant technical metadata.

Senior Consultant

Analyst

Adhoc Analyst

Executive Assistant

• Lead risk analytics strategy at the highest levels of the organization.
• Drive organizational change through effective risk analytics management.
• Represent the organization in external risk analytics forums.
• Mentor senior leaders in risk analytics management.

Senior Assistant Vice President

• Define enterprise reference architectures for Agentic AI and LLM-powered platforms, including: single-agent and multi-agent systems, tool-calling and function orchestration, memory, planning, and execution layers.
• Own architectural decisions for Claude / Claude Code and other enterprise-grade LLMs, including model selection, deployment patterns, and cost-latency trade-offs.
• Design secure-by-default GenAI systems incorporating guardrails and policy enforcement, data privacy, PII handling, and prompt safety.
• Architect large-scale RAG solutions, covering data ingestion and curation pipelines, chunking and embedding strategies, vector databases and hybrid search, evaluation and feedback loops.
• Partner with Data Engineering teams to ensure data quality, lineage, observability, and governance for AI-driven systems.
• Drive production readiness of GenAI systems: API-first design (FastAPI / REST / event-driven), CI/CD for LLM workflows, monitoring, evaluation, and cost tracking.
• Establish engineering standards, reusable frameworks, and accelerators for faster adoption across EXL accounts.
• Review and influence cloud architecture (Azure / AWS / GCP) for scalable and compliant AI deployments.
• Act as a technical authority for GenAI across delivery teams and client engagements.
• Mentor senior engineers, tech leads, and architects on agentic patterns and advanced LLM engineering.
• Partner with clients, product owners, and domain SMEs to shape AI roadmaps, solution designs, and value articulation.

AI Lead, Architect – Claude, LLM Skills

Data Engineer

• Design and implement scalable data lake/lakehouse architectures on AWS using S3 and Iceberg.
• Develop and optimize ETL/ELT pipelines using PySpark and orchestrate workflows with Apache Airflow.
• Integrate and manage Snowflake as the enterprise data warehouse and business layer for analytics.
• Ensure data quality, governance, security, and performance across the data platform.
• Lead technical design discussions, define best practices, and drive data engineering standards.
• Mentor and guide a team of data engineers, fostering a collaborative and high-performance culture.
• Collaborate with cross-functional teams including data analysts, data scientists, and business stakeholders.

Lead AWS Data Engineer

Product Manager

• Define and manage the AI product roadmap for AR capabilities: cash application automation, deductions management, credit risk scoring, collections prioritization, and dispute resolution.
• Translate AR business requirements into AI architectures, orchestration frameworks, and product features that deliver measurable outcomes (DSO reduction, auto-match rate improvement).
• Design Generative AI solutions using state-of-the-art models (GPT-4o, Claude, Llama 3) for AR-specific tasks: automated remittance parsing, dispute email generation, and intelligent customer communication.
• Architect Agentic AI workflows for autonomous AR operations: cash matching agents, dispute classification agents, and collections prioritization agents using LangChain, AutoGen, and CrewAI.
• Guide data science and ML engineering teams in building, fine-tuning, and deploying LLMs and ML models for AR use cases: payment prediction, credit risk scoring, and anomaly detection.
• Architect scalable AI pipelines on cloud platforms (AWS, Azure, GCP); drive adoption of LLM orchestration stacks and MLOps practices for production AR AI systems.
• Collaborate with client Finance technology teams and AR operations leads to identify AI-driven automation opportunities and co-define solution requirements.
• Write detailed Product Requirement Documents (PRDs), user stories, and acceptance criteria for AI-powered AR features; manage and prioritize the product backlog.

Product Manager, F&A

Sales

• The role is a front-end growth leadership position focused on expanding EXL’s market presence by winning and scaling strategic client engagements across priority sectors
• Drive revenue growth through Data, Digital, and AI-led solutions, positioning EXL as a transformation partner
• Operate as a trusted advisor to CXOs, leveraging deep understanding of client context and buying behaviour to influence decisions
• Own the end-to-end deal lifecycle—from origination to closure and early-stage expansion
• Manage complex sales cycles and ensure strong post-win ramp-up
• Orchestrate cross-functional teams to deliver integrated, outcome-driven solutions aligned to client priorities
• Accountable for driving consistent new revenue growth and contributing to long-term portfolio expansion

Vice President, Sales & Client Development – EMEA, Travel, Hospitality, Shipping & Logistics

Data Scientist

• Assist in analyzing business processes
• Support development of business requirements
• Document analysis processes
• Perform basic data analysis
• Collaborate with senior analysts.

• Managing Customer engagement, Liaise with Clients Engagement managers oversee the customer service operations within a business, dealing with customers directly and often as their first point of contact.
• Application supports: On-call rotation, Receiving and acknowledging tickets, Incident triaging and priority assessment, Assignment of incidents, Incident resolution, Application monitoring and preventive maintenance, Weekly status and SLA reporting
• Incident and Problem management: Analyse, troubleshoot and work with team to resolve complex production application defects, Identify root cause and devise and implement permanent solutions to recurring defects
• Build and maintain application monitoring, optimisation and utilisation
• Lead the support team to achieve compliance with SLAs, work within commercial and resource constraints; prioritisation, resourcing, scheduling and QA across all activities.
• Communicate with stakeholders in case of delays or SLA breaches.
• Maintain and own defect management and incident resolution process.
• Follow up with third party vendors for issue resolution and prevention, if required.
• Perform weekly incident/bug review meetings with application team.
• Perform custom, ad-hoc data analysis and reporting based on stakeholder requests within SLA.
• Support weekly and monthly customer report processing.
• Conduct regular presentations to team/stakeholders.
• Good experience of working in Agile methodologies.
• Manage process improvements.
• Resolve defects within the defined service levels.
• Review and take actions on operational metrics reporting.
• Follow SDLC standards, source code management, and release management procedures.
• Excellent verbal and written communication skills.
• Mentor offshore team members in Process and Delivery of work items within defined schedule.
• Ability to be eloquent and present different point-of-view during production support and releases.
• Code Management & working experience in GIT Hub, Azure DevOps or Similar.
• Working knowledge of Azure Data Factory and Azure Data Bricks.
• ETL process and tools.
• DevOps practices such as infrastructure as code, continuous integration and automated deployment.
• Spark or Hadoop or Hive (incl. derivatives like pySpark (preferred), Spark Scala or Spark SQL) or Similar.
• SQL queries & working experience in SQL code or Stored Procedures or Functions or Views or Similar.
• Performance tuning, design or architecture of new solutions with Data Warehouse Architects.
• Data warehouse working experience of concepts and development using SQL on single (SQL Server, Oracle or Similar) and parallel platforms (Azure SQL Data Warehouse Synapse or Snowflake)

• Ensure the accuracy and integrity of balance sheet accounts by performing detailed reconciliations.
• Identify discrepancies, resolve issues, and maintain accurate financial records to support financial reporting and compliance.
• Perform expense Allocations, Re-class entries, Book Adjustments and Accrual entries
• Complete all close related tasks assigned on timely basis adhering to the business quality.