The Role
Owns the AI product roadmap and delivery for Accounts Receivable and Order-to-Cash automation. Responsibilities include designing generative AI and agentic workflows, guiding ML engineering teams, defining product requirements, managing Agile backlogs and releases, tracking product metrics, engaging Finance clients, and developing solution demonstrations and ROI materials. The role requires strong AI/ML engineering, MLOps, Python, cloud, financial technology, and Accounts Receivable expertise.
Summary Generated by Built In
Short Description for Internal Candidates
Responsibilities Short Description for Internal Candidates
Qualifications
We are looking for a technically strong Manager – AI Product Experience to own the design and delivery of AI-powered product features for the Accounts Receivable and Order-to-Cash domain. The role combines hands-on AI engineering capability with product management ownership, requiring the incumbent to work closely with data scientists, software engineers, and Finance stakeholders to ship intelligent AR automation that reduces DSO, improves cash application accuracy, and enhances collections intelligence.
KEY RESPONSIBILITIES
Product Design & AI Strategy
- 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.
Technical Leadership
- 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.
- Stay current on advancements in Generative AI, NLP, and ML engineering frameworks (PyTorch, TensorFlow, LangChain, LlamaIndex, LangGraph); proactively incorporate innovations into the AR AI product.
- Ensure AI solutions meet compliance, security, and auditability standards applicable to receivables and financial data.
Client Engagement
- Collaborate with client Finance technology teams and AR operations leads to identify AI-driven automation opportunities and co-define solution requirements.
- Present Generative AI use cases, proof-of-concept demos, and ROI narratives to both technical and non-technical client audiences.
- Act as a trusted product and AI advisor to clients on the technical, ethical, and operational considerations of deploying AR AI solutions.
- Support Sr. AVP in first-engagement solutioning, client workshops, and RFP/RFI responses for AR transformation engagements.
Product Management & Delivery
- Write detailed Product Requirement Documents (PRDs), user stories, and acceptance criteria for AI-powered AR features; manage and prioritize the product backlog.
- Conduct user discovery interviews with AR operations teams, controllers, and treasury leads to surface pain points and validate product hypotheses.
- Lead sprint planning, grooming, and retrospectives with cross-functional Agile engineering teams; manage release readiness across QA, UX, and compliance workstreams.
- Define and track product metrics: cash application hit rate, auto-match accuracy, DSO improvement, collector productivity, and AI model performance.
- Author product collateral: demo scripts, solution one-pagers, sales enablement decks, and ROI calculators for AR AI solutions.
QUALIFICATIONS
Education & Experience (9–12 Years)
- B.Tech or M.Tech in Computer Science, Software Engineering, Data Science, or a related technical discipline.
- 9–12 years of total experience, with at least 4–5 years in AI/ML product engineering and 3+ years of Finance technology experience with strong AR/O2C domain exposure.
- 3–4 years of hands-on experience with Generative AI and Large Language Models: prompt engineering, LLM fine-tuning, and building applications using LangChain, LlamaIndex, or RAGAS.
- Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) and applying ML models to finance classification, prediction, and anomaly detection use cases.
- Working experience with Agentic AI concepts: Autonomous Agents, AutoGen, CrewAI, LangGraph; ability to design and deploy multi-step AI agent workflows for AR automation.
- Experience in ML Engineering and MLOps: model deployment, performance monitoring, versioning, and retraining pipelines (MLflow, SageMaker, Azure ML).
- Strong software engineering skills in Python; experience building AI-powered APIs, integrations, and cloud-native microservices.
- Proven track record of delivering AI product features from ideation through production deployment in an Agile environment; CSPO or equivalent certification preferred.
Skills & Competencies
- Solid functional knowledge of Accounts Receivable: cash application, collections, credit management, deductions, billing, and ERP AR modules (SAP FSCM, Oracle Fusion AR, HighRadius, Esker).
- Experience with NLP techniques for remittance advice parsing, dispute letter classification, and payment confirmation extraction from unstructured documents.
- Understanding of Vector Databases and Graph Databases and their application in AR knowledge retrieval, semantic matching, and customer intelligence.
- Familiarity with cloud AI services (AWS Bedrock, Azure OpenAI, GCP Vertex AI) for both model inferencing and fine-tuning AR-specific models.
- Strong SQL and Python proficiency; experience with BI/analytics tools (Tableau, Power BI) for data-driven product and model performance decisions.
- Excellent communication skills: ability to explain Generative AI concepts and product value to non-technical Finance audiences.
- Experience working cross-functionally across engineering, data science, UX, and Finance SME teams; comfortable managing competing priorities in a fast-paced product environment.
- Participation in RFI/RFP, thought leadership, and client-facing solutioning activities preferred.
Responsibilities Short Description for Internal Candidates
We are looking for a technically strong Manager – AI Product Experience to own the design and delivery of AI-powered product features for the Accounts Receivable and Order-to-Cash domain. The role combines hands-on AI engineering capability with product management ownership, requiring the incumbent to work closely with data scientists, software engineers, and Finance stakeholders to ship intelligent AR automation that reduces DSO, improves cash application accuracy, and enhances collections intelligence.
KEY RESPONSIBILITIES
Product Design & AI Strategy
- 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.
Technical Leadership
- 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.
- Stay current on advancements in Generative AI, NLP, and ML engineering frameworks (PyTorch, TensorFlow, LangChain, LlamaIndex, LangGraph); proactively incorporate innovations into the AR AI product.
- Ensure AI solutions meet compliance, security, and auditability standards applicable to receivables and financial data.
Client Engagement
- Collaborate with client Finance technology teams and AR operations leads to identify AI-driven automation opportunities and co-define solution requirements.
- Present Generative AI use cases, proof-of-concept demos, and ROI narratives to both technical and non-technical client audiences.
- Act as a trusted product and AI advisor to clients on the technical, ethical, and operational considerations of deploying AR AI solutions.
- Support Sr. AVP in first-engagement solutioning, client workshops, and RFP/RFI responses for AR transformation engagements.
Product Management & Delivery
- Write detailed Product Requirement Documents (PRDs), user stories, and acceptance criteria for AI-powered AR features; manage and prioritize the product backlog.
- Conduct user discovery interviews with AR operations teams, controllers, and treasury leads to surface pain points and validate product hypotheses.
- Lead sprint planning, grooming, and retrospectives with cross-functional Agile engineering teams; manage release readiness across QA, UX, and compliance workstreams.
- Define and track product metrics: cash application hit rate, auto-match accuracy, DSO improvement, collector productivity, and AI model performance.
- Author product collateral: demo scripts, solution one-pagers, sales enablement decks, and ROI calculators for AR AI solutions.
QUALIFICATIONS
Education & Experience (9–12 Years)
- B.Tech or M.Tech in Computer Science, Software Engineering, Data Science, or a related technical discipline.
- 9–12 years of total experience, with at least 4–5 years in AI/ML product engineering and 3+ years of Finance technology experience with strong AR/O2C domain exposure.
- 3–4 years of hands-on experience with Generative AI and Large Language Models: prompt engineering, LLM fine-tuning, and building applications using LangChain, LlamaIndex, or RAGAS.
- Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) and applying ML models to finance classification, prediction, and anomaly detection use cases.
- Working experience with Agentic AI concepts: Autonomous Agents, AutoGen, CrewAI, LangGraph; ability to design and deploy multi-step AI agent workflows for AR automation.
- Experience in ML Engineering and MLOps: model deployment, performance monitoring, versioning, and retraining pipelines (MLflow, SageMaker, Azure ML).
- Strong software engineering skills in Python; experience building AI-powered APIs, integrations, and cloud-native microservices.
- Proven track record of delivering AI product features from ideation through production deployment in an Agile environment; CSPO or equivalent certification preferred.
Skills & Competencies
- Solid functional knowledge of Accounts Receivable: cash application, collections, credit management, deductions, billing, and ERP AR modules (SAP FSCM, Oracle Fusion AR, HighRadius, Esker).
- Experience with NLP techniques for remittance advice parsing, dispute letter classification, and payment confirmation extraction from unstructured documents.
- Understanding of Vector Databases and Graph Databases and their application in AR knowledge retrieval, semantic matching, and customer intelligence.
- Familiarity with cloud AI services (AWS Bedrock, Azure OpenAI, GCP Vertex AI) for both model inferencing and fine-tuning AR-specific models.
- Strong SQL and Python proficiency; experience with BI/analytics tools (Tableau, Power BI) for data-driven product and model performance decisions.
- Excellent communication skills: ability to explain Generative AI concepts and product value to non-technical Finance audiences.
- Experience working cross-functionally across engineering, data science, UX, and Finance SME teams; comfortable managing competing priorities in a fast-paced product environment.
- Participation in RFI/RFP, thought leadership, and client-facing solutioning activities preferred.
Qualifications
Bachelor's/Master's
Skills Required
- Bachelor’s or master’s degree in Computer Science, Software Engineering, Data Science, or a related technical discipline
- 9–12 years of total professional experience
- 4–5 years of experience in AI/ML product engineering
- 3 or more years of Finance technology experience with strong Accounts Receivable or Order-to-Cash exposure
- 3–4 years of hands-on Generative AI and Large Language Model experience, including prompt engineering, fine-tuning, and application development
- Experience with LangChain, LlamaIndex, or RAGAS
- Hands-on experience with PyTorch and TensorFlow
- Experience applying machine learning to finance classification, prediction, and anomaly detection
- Experience designing and deploying agentic AI workflows using AutoGen, CrewAI, or LangGraph
- ML engineering and MLOps experience, including deployment, monitoring, versioning, and retraining pipelines
- Experience with MLflow, SageMaker, or Azure Machine Learning
- Strong Python software engineering skills and experience building AI APIs, integrations, and cloud-native microservices
- Track record delivering AI product features from ideation through production in an Agile environment
- Functional knowledge of Accounts Receivable, cash application, collections, credit management, deductions, billing, and ERP AR modules
- Experience with NLP for remittance parsing, dispute classification, and payment confirmation extraction
- Understanding of vector and graph databases
- Familiarity with AWS Bedrock, Azure OpenAI, or Google Vertex AI
- Strong SQL proficiency
- Experience with Tableau or Power BI
- Excellent communication skills with technical and non-technical Finance audiences
- Experience collaborating across engineering, data science, UX, and Finance SME teams
- CSPO or equivalent product certification
- Participation in RFI/RFP, thought leadership, and client-facing solutioning activities
EXL Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about EXL and has not been reviewed or approved by EXL.
-
Healthcare Strength — Medical plan options include HSA, HRA, and PPO choices, with additional health-related programs such as disability coverage and an employee assistance program. Feedback suggests these options provide meaningful flexibility and coverage breadth for many roles.
-
Leave & Time Off Breadth — Up to 20 days of paid time off, plus federal holidays and paid sick and safe time, are highlighted. Feedback suggests this leave structure is a notable benefit alongside core compensation.
-
Retirement Support — A traditional and Roth 401(k) with up to a 3% company match and full vesting after one year is described. Feedback suggests this match and vesting timeline help bolster long-term savings.
EXL Insights
Similar Jobs
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
What We Do
Choosing a digital partner is about more than capabilities — it’s about collaboration and character. Unrealistic overhauls and off-the-shelf products ignore what matters most — your unique needs, culture, goals, and your legacy data and technology environments. At EXL, our collaboration is built on ongoing listening and learning to adapt our methodologies. We’re your business evolution partner—tailoring solutions that make the most of data to make better business decisions and drive more intelligence into your increasingly digital operations. Whether your goals are scaling the use of AI and digital, redesign operating models, or driving better and faster decisions, we’re here to partner with you to help you gain—and maintain—competitive advantage with efficient, sustainable models at scale. Our expertise in transformation, data science, and change management helps make your business more efficient and effective, improve customer relationships and enhance revenue growth. Instead of focusing on multi-year, resource- and time-intensive platform designs or migrations, we look deeper at your entire value chain to integrate strategies with impact. We use our specialization in analytics, digital interventions, and operations management—alongside deep industry expertise — to deliver solutions that help you outperform the competition. At EXL, it’s all about outcomes—your outcomes—and delivering success on your terms. Share your goals with us and together, we’ll optimize how you leverage data to drive your business forward. For more information, visit www.exlservice.com.
