Product Manager

About the role

What you'll do

• 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.

What you'll bring

• 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.