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

QA Engineer

• Design and execute test strategies for data pipelines built using PySpark and orchestrated via Airflow.
• Validate data ingestion, transformation, and storage in S3-based lakehouse (Iceberg tables).
• Perform data reconciliation between source systems, lakehouse, and Snowflake business layer.
• Develop automated test frameworks for large-scale data validation.
• Ensure data quality, completeness, consistency, and performance across pipelines.
• Collaborate with data engineers and stewards to enforce data quality rules and governance standards.
• Identify, track, and resolve defects in data workflows and reporting layers.

Quality Assurance Engineer

• Drafting an accounting summary of key terms in new venture transaction agreements
• Researching the launch status of tokens
• Preparing supporting documentation on follow on investments
• Reconciliation of venture transactions on USDC/USDT
• Preparing draft journal entries
• Preparing cash flow and liquidity tracker
• Reconciliation of investment GL account balances
• Variance analyses on balance sheet and income statement accounts
• Preparing bonus pool calculations
• Preparing classification documentation and vesting schedules
• Preparing gain/loss calculations for reclassification
• Preparing calculation of staking revenues and token activities
• Obtaining token prices for Ventures tokens
• Calculating mark-to-market adjustments
• Preparing draft journal entries for token sales and other activities
• Preparing Digital Asset cash flow documentation
• Performing impairment analysis for investments
• Preparing draft ventures investment disclosures and support for slides
• Preparing updates to SEC Checklist
• Reconciliation of digital asset investment balances

Application Engineer

• Monitor, maintain, and support .NET applications in a production environment.
• Troubleshoot and resolve application issues reported by users or identified through monitoring tools.
• Collaborate with development teams to deploy patches, updates, and new releases.
• Troubleshoot and resolve database-related issues, ensuring data integrity and availability.
• Write and optimize SQL queries, stored procedures, and functions as needed.
• Provide support to end-users by addressing application-related queries and issues.
• Conduct user training sessions as needed to improve user proficiency with the applications.
• Identify and address performance bottlenecks to improve application efficiency.
• Maintain comprehensive documentation of application configurations, procedures, and troubleshooting guides.
• Identify opportunities for process improvements and contribute to the development and implementation of best practices.
• Stay updated with industry trends, technologies, and best practices in .NET and SQL development and support.

• Oversee data advisory projects
• Collaborate with clients and teams to deliver insights