KEY RESPONSIBILITIES

QUALIFICATIONS

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.

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.

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.

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.