AI Engineer

• Design, develop, and deploy AI solutions leveraging LLMs (GPT, Claude, open-source models).
• Engineer and optimize prompts to improve accuracy and relevance across use cases.
• Collaborate with cross-functional teams and retail/CPG stakeholders to identify and deliver high-impact AI solutions.
• Monitor, fine-tune, and maintain models to ensure performance, scalability, and business alignment.
• Implement AI/ML-driven efficiencies in BI migration initiatives (e.g., MicroStrategy to Metabase).
• Ensure data governance, quality, and security throughout AI and migration processes.
• Stay current with advancements in the AI/LLM ecosystem to drive continuous innovation.

AI Engineer – Retail, CPG Domain

Procurement

• Partner with client Procurement, AP, Finance, and Legal to validate contract-to-PO and invoice compliance versus pricing, rebates/discounts, and SLA terms.
• Identify value leakage and overbilling patterns (rate/UOM errors, missed discounts, unauthorized changes, non-compliant billing, missed SLA credits).
• Mentor Managers and Analysts and build playbooks for compliance, recovery, and governance.
• Validate system-identified exceptions; recommend escalation paths and recovery actions.
• Quantify opportunities, track recoveries to closure, document root causes and corrective actions, monitor and report leakage trends
• Translate contract structures, pricing, and SLAs into clear, testable business rules; communicate findings to executive stakeholders.
• Aligning findings with governance, controls, and documentation standards in partnership with Finance and Legal.
• Help with the continuous improvement of EXL product.

Procurement Specialist – Manager

Executive Assistant

• Define, own, and evolve the AI product roadmap for healthcare claims solutions—spanning claims adjudication automation, claims adjustment, payment integrity, COB/subrogation, provider issue resolution, and FWA detection.
• Translate complex claims operational challenges into AI-first product strategies with clear business cases, ROI frameworks, and measurable KPIs (e.g., auto-adjudication rate, denial accuracy, overpayment recovery yield).
• Drive solutions from ideation → POC → MVP → production scale, using agile execution and business-centric prioritization.
• Maintain a competitive landscape matrix and continuously benchmark against market players (Optum/Change Healthcare, Cotiviti, Cognizant TriZetto, HealthEdge, Conduent, etc.) to inform differentiation strategy.
• Partner with sales, finance, and leadership to determine pricing, packaging, and go-to-market approach (managed services, SaaS, outcome-based/gainshare models).
• Drive the transition from traditional rules-engine claims processing to AI-augmented adjudication—automating claim edits, benefit configuration interpretation, provider contract parsing, and payment rule application.
• Apply deep knowledge of end-to-end claims operations—claim intake, edits, adjudication, pricing, payment, adjustment, appeals, grievances, provider disputes, and overpayment recovery—to identify high-impact AI use cases.
• Embed AI solutions into core claims platforms and systems (QNXT, Facets, Amisys, HealthRules Payer, CSC/DXC, etc.) through platform-agnostic integration and API-first design.

Senior Assistant Vice President – Healthcare Claims AI, Technical Product Leader

Auditor

• Conduct clinical reviews to verify the accuracy of skilled nursing facility billing.
• Apply CMS clinical guidelines and payer-provider contract terms and policies to a variety of claim scenarios to identify overpayments for claims paid under PDPM, RUG, and contracted per diem payment methodologies.
• Apply ICD-10 coding guidelines to validate diagnoses coded on Minimum Data Set assessment.
• Document accurate audit results in a clear, concise, and effective manner using applicable EXL tools, CMS guidelines, payer contractual terms and policies.
• Utilize proprietary workflow systems efficiently and accurately to make audit determinations, generate audit rationales and move claims through workflow process correctly.
• Meet or exceed EXL established program productivity and quality goals.
• Demonstrate knowledge of and compliance with changes and updates to clinical guidelines, reimbursement trends, and client processes and requirements.
• Comply with all EXL policies and procedures including HIPAA and other state and federal regulations.

Auditor III, Healthcare – Skilled Nursing Facility / Resource Utilization Group

Manager

• Manages overall team performance to ensure SLA targets are met
• Manage team and take corrective steps when necessary.
• Client interaction where required at level of supervisor
• Handle escalations
• Provides inputs on process and system to team member

Assistant Manager

Project Manager

• Plan, monitor & manage operational delivery to ensure adherence to agreed quality parameters & timelines.
• Implement work management trackers, define roles & responsibilities of team members and roll out a work allocation / delivery plan to track completion.
• Work with onshore managers to agree on scope of work, key stakeholders, escalation matrices, project milestones & timelines.
• Design & Implement management information systems (MIS) for periodic status updates and to identify trends around business processes identified as high on risk / low on controls.
• Drive a culture of Risk and Governance across business processes.
• Champion customer service to ensure that compliance is integrated in business processes.
• Demonstrate excellent project management skills, inspire teamwork and responsibility with engagement team members, and use current technology/tools to enhance the effectiveness of deliverables and services.

Marketing Analytics, Project Manager

Full-stack Engineer

• Lead and support multiple client engagements within the Life Insurance and Annuity domain.
• Collaborate with business stakeholders, project managers, business analysts, and development teams to analyze business requirements and design effective technical solutions.
• Participate in project planning, estimation, and solution architecture discussions.
• Conduct design reviews and provide recommendations on application architecture, data flow, and system integration.
• Ensure adherence to Project Life Cycle Methodology, IT control policies, and corporate architecture standards.
• Prepare and maintain high-level and low-level technical design documents and specifications.
• Provide technical leadership to development and support teams across onshore and offshore locations.
• Drive code quality through code reviews, best practices, and development standards.
• Identify opportunities for process improvement, automation, and adoption of emerging technologies.
• Stay current with industry trends, technologies, and market developments related to Life Insurance and Annuity products.

Onshore Technical Lead

Machine Learning Engineer

• Design, develop, and deploy end-to-end ML pipelines covering data ingestion, transformation, feature engineering, model training, evaluation, and production deployment.
• Deploy and scale ML models on cloud platforms such as AWS (SageMaker, EKS, Lambda) or GCP (Vertex AI, GKE, Cloud Functions), ensuring robust and cost-efficient architectures.
• Build and maintain CI/CD/CT pipelines using tools like GitHub Actions, Jenkins, or cloud-native services to automate model training, testing, and deployment.
• Containerize applications using Docker and orchestrate using Kubernetes, while managing infrastructure through Terraform or CloudFormation.
• Implement model lifecycle management practices, including model registries, versioning, and feature stores (e.g., MLflow, Feast), and establish strong observability frameworks using Prometheus and Grafana.
• Develop monitoring systems to track ML performance metrics, data drift, model drift, and overall model health, ensuring timely retraining and optimization.
• Collaborate closely with data scientists to productionize ML models for real-time and batch inference, enable A/B testing where applicable, and ensure smooth delivery of client-facing solutions. Provide mentorship to junior engineers and drive adoption of best practices in MLOps and software engineering.

Senior ML Engineer / MLOps Engineer

Data Scientist

• Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
• Build adaptive decisioning systems using techniques such as:
• Q-Learning
• Deep Q Networks (DQN)
• Policy Gradient Methods
• Contextual Bandits
• Markov Decision Processes (MDP)
• Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.
• Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
• Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.
• Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.
• Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.

Data Scientist – Reinforcement Learning

• Design and develop Reinforcement Learning models to optimize collections strategies, customer treatment paths, and recovery outcomes.
• Build adaptive decisioning systems using techniques such as:
• - Q-Learning
• - Deep Q Networks (DQN)
• - Policy Gradient Methods
• - Contextual Bandits
• - Markov Decision Processes (MDP)
• Develop sequential and behavioral models for customer engagement, repayment prediction, and collections prioritization.
• Apply stochastic modeling and probabilistic methods to optimize dynamic treatment strategies under uncertainty.
• Collaborate with business stakeholders to translate collections and risk management problems into scalable AI/ML solutions.
• Build and maintain machine learning pipelines in Databricks or similar distributed computing environments.
• Conduct experimentation, simulation, and offline policy evaluation to validate RL strategies before deployment.
• Work with large-scale structured and unstructured datasets to derive actionable insights and improve operational performance.
• Partner with engineering and MLOps teams to deploy and monitor production-grade ML/RL models.
• Mentor junior data scientists and promote best practices in modeling, experimentation, and AI governance.

Senior Data Scientist – Reinforcement Learning

Data Engineer

• Lead and oversee project delivery, ensuring timely completion with high quality.
• Collaborate with clients and offshore leads to gather requirements and define high-level designs.
• Direct development and unit testing activities while maintaining strong alignment with project goals.
• Communicate effectively with stakeholders, providing regular updates on task and project statuses.
• Identify, track, and address project risks or issues promptly.
• Work closely with management to ensure seamless execution of tasks.
• Provide technical guidance to the team, outlining design, implementation, and delivery strategies.

Lead Data Engineer – AWS

Collections

• Demonstrates ability to perform all appropriate tasks within Collection Department. **
• Ability to manage their portfolio in High Radius regarding day-to-day activities appropriate for collection of aged accounts receivable assuring all functions are covered within guidelines established, elevating as appropriate to resolve issues timely. Requires moderate assistance from Collections Manager. **
• Execute plan established with their Collections Manager to contact all customers within portfolio responsibility reducing A/R balances to meet A/R goals of Current A/R aging balance of 90%. **
• High level of understanding of Dispute Resolution/ Problem set up for items forwarded to field for resolution including appropriate follow up. **
• Customer transaction management and reconciliation of invoices, unapplied cash, credit & debit memos. **
• Route customer service emails and phone calls to the appropriate department for resolution **
• Develops multi-level relationships in Employbridge organization to enhance ability to work with senior field management regarding resolution of elevated issues. **
• Demonstrates company and department core values, operating principles, and customer service standard. **
• High level of knowledge of accounts over credit limit as it relates to customer activity current and ongoing. **
• Other duties as assigned.

Senior Executive – Collections

Application Engineer

• Design, develop, and support enterprise-grade applications using .NET, .NET Core, and Python, ensuring scalability, maintainability, and performance.
• Define and implement robust application architecture patterns aligned with business, security, and operational requirements.
• Lead the design of modern data architectures, including relational databases, NoSQL/document stores, star schemas, data lakes, and streaming data pipelines.
• Translate business and technical requirements into end-to-end solution designs that integrate application, data, and infrastructure components effectively.
• Provide guidance on infrastructure topologies for both on-premises and cloud-based environments, including virtual machines, containers, Kubernetes/AKS, and Azure-native services.
• Drive adoption of strong SDLC and DevOps practices, including CI/CD, code quality controls, release automation, environment management, and agile delivery methodologies.
• Collaborate with cross-functional teams including business stakeholders, architects, developers, data engineers, and operations teams to deliver high-quality technology solutions.
• Ensure solutions are designed with appropriate focus on resilience, scalability, security, and operational efficiency.
• Troubleshoot complex technical issues across application, data, and infrastructure layers, and provide practical resolution approaches.
• Work effectively in high-pressure environments, managing multiple priorities while maintaining delivery quality and timeliness.
• Leverage industry knowledge to align technology solutions with insurance domain requirements, processes, and business objectives.

Artificial Intelligence

• Design and implement AI-driven solutions, including LLM-based applications, agentic workflows, and automation systems
• Build multi-step data pipelines for document processing, transformation, validation, and error handling
• Develop semantic search and RAG-based solutions using embeddings, vector databases, and retrieval pipelines
• Engage with business stakeholders to understand complex workflows and identify opportunities for AI adoption
• Translate business requirements into scalable technical architectures and solution designs
• Ensure integration with enterprise platforms, data sources, and governance systems

Senior Associate – AI Consulting, Insurance

• Design and implement agentic AI systems (single and multi-agent) with tool use, memory, and fallback mechanisms.
• Build production-grade agents using frameworks like LangGraph, AutoGen, CrewAI, or custom LLM orchestration layers.
• Implement agent reasoning loops including planning, tool selection, execution, observation, and re-planning with safety guardrails.
• Develop prompt and context engineering strategies for reliable, grounded LLM outputs.
• Design agent orchestration workflows include task routing, parallel execution, state management, retries, and human-in-the-loop escalation.
• Build evaluation frameworks for LLMs and agents including automated testing, adversarial testing, and performance benchmarking.
• Implement retrieval and grounding using vector databases, embeddings, and knowledge graphs for contextual accuracy.
• Ensure observability of agent systems by tracing LLM calls, tool usage, and decision paths using monitoring tools.
• Apply security and governance controls including prompt injection defense, access control, and safe tool execution.
• Optimize agent systems for latency, cost, and scalability in production environments.
• Build CI/CD pipelines for agent workflows including versioning, testing, and controlled deployments.
• Integrate agents with enterprise systems and APIs to automate end-to-end business workflows.
• Design feedback loops using production traces and evaluation signals to continuously improve agent performance.
• Experience with Model Context Protocol (MCP) systems to design database connections, integrate APIs, and enable secure tool orchestration for AI agents.
• Hands-on experience in fine-tuning LLMs for domain-specific applications using LoRA, PEFT, QLoRA, RLHF, instruction tuning, and other parameter-efficient adaptation techniques.
• Stay current with emerging agentic AI frameworks, research, and best practices for production deployment.