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Tech Stack
Tools & technologiesAWSAzureCloudGoogle Cloud PlatformMicroservicesPythonPyTorchTensorflow
About the role
Key responsibilities & impact- 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.
Requirements
What you’ll need- 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.
Benefits
Comp & perks- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Remote work options
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Generative AILarge Language Modelsprompt engineeringLLM fine-tuningdeep learning frameworksPyTorchTensorFlowAI product engineeringMLOpsmodel deployment
Soft Skills
collaborationcommunicationproduct managementprioritizationproblem-solvingleadershipagile methodologydocumentationteam guidancesolution definition
Certifications
B.TechM.TechCSPO