We are seeking a seasoned AVP – AI Product Experience to own the design, build, and deployment of AI-powered product capabilities for the Accounts Payable domain. The role demands deep technical grounding in Generative AI, Agentic AI, and LLM engineering, combined with strong product leadership to translate AP process complexity into scalable, intelligent software solutions. The incumbent will work closely with engineering, data science, and client-facing teams to define and ship world-class AI experiences on EXL's Finance AI platform.
Description-
KEY RESPONSIBILITIES
Product Vision & Platform Strategy
Own end-to-end product vision and multi-year roadmap for AI-powered AP solutions: intelligent invoice processing, three-way matching, supplier onboarding, exception management, and payment orchestration.
Define product architecture and feature set for Generative AI and Agentic AI capabilities embedded within AP workflows, ensuring alignment with EXL's Finance AI platform strategy.
Participate in deep architectural discussions and design exercises to create world-class AP AI solutions, ensuring they are designed for successful deployment on cloud platforms (AWS, Azure, GCP).
Drive build-vs-buy-vs-partner decisions, evaluating AI frameworks and vendor ecosystems against in-house capability development.
Solution Design & AI Architecture
Design Generative AI solutions leveraging state-of-the-art models (GPT-4o, Claude, Llama 3) to automate AP-specific tasks such as invoice data extraction, PO matching, and vendor communication.
Architect agentic AI workflows for autonomous AP operations: self-healing exception agents, multi-step approval orchestration agents, and real-time supplier query resolution agents.
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Translate complex AP business requirements into AI architectures, orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI), and scalable deployment pipelines (MLOps, MLflow).
Design NLP and Intelligent Document Processing (IDP) capabilities for structured and unstructured AP documents: invoices, POs, GRNs, contracts, and remittance advices.
Ensure AI solutions meet compliance, security, and scalability standards including SOX, GDPR, and enterprise data governance requirements.
Technical Leadership
Guide technical teams of data scientists, ML engineers, and software engineers in building, fine-tuning, and deploying LLMs and multimodal AI models for AP use cases.
Architect scalable, secure AI pipelines on cloud platforms; drive adoption of deep learning frameworks (PyTorch, TensorFlow) and LLM orchestration stacks (LangChain, LlamaIndex, LangGraph).
Stay current on advancements in Generative AI, agentic systems, NLP, and ML engineering; translate emerging trends into product features and competitive differentiation.
Drive engineering excellence: code quality standards, MLOps practices, model versioning, and production monitoring across AP AI models.
Client Engagement & Go-to-Market
Interact directly with client CTO, CPO, and Finance technology leaders to understand AP pain points, showcase EXL's AI capabilities, and co-create transformation roadmaps.
Lead client solutioning during first engagements: define scope, architect proof-of-concepts, and present AI use cases to both technical and business audiences.
Author and contribute to EXL customer-facing publications: whitepapers, workshops, demo environments, and proof-of-concept accelerators for AP AI.
Represent EXL at industry forums, analyst briefings (Gartner, Forrester), and RFP/RFI responses as the Finance AI product SME.
Build deep relationships with senior technical stakeholders at client organizations, enabling them to become AI advocates internally.
Product Management & Delivery
Define and maintain a prioritized product backlog with detailed PRDs, user stories, and acceptance criteria aligned to AP domain outcomes.
Establish product KPIs: straight-through processing (STP) rates, invoice auto-match accuracy, exception reduction, and cycle time improvements.
Capture and share best-practice knowledge across EXL's AI product and solutions architect communities.
Mentor a cross-functional team of product managers, solution architects, ML engineers, and finance SMEs.
QUALIFICATIONS
Education & Experience (14+ Years)
B.Tech or M.Tech in Computer Science, Software Engineering, Data Science, or related technical discipline.
14–16 years of total experience, with at least 6–8 years in AI/ML product engineering and 8+ years in Finance technology with deep AP/P2P domain ownership.
8+ years of hands-on experience in Generative AI and Large Language Models – finetuning LLMs, prompt engineering, and building with LLM orchestration frameworks (LangChain, LlamaIndex, RAGAS, LangGraph).
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) as evidenced by production deployments or public repositories.
Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine-tuning for finance-specific models.
Strong experience in Agentic AI: Autonomous Agents, AutoGen, CrewAI, LangGraph workflow design; familiarity with NVIDIA AI Blueprints and Google AI Agents.
Experience in ML Engineering and MLOps, including MLflow, model versioning, drift monitoring, and retraining pipelines.
Proven ability to deliver complex AI products from ideation through production; track record of driving go-to-market for enterprise AI solutions.
Strong software engineering skills in Python, with experience building and integrating AI APIs, microservices, and cloud-native architectures.
Skills & Competencies
Deep functional knowledge of Accounts Payable: P2P lifecycle, ERP integrations (SAP S/4HANA, Oracle Fusion, Coupa, Ariba), invoice automation, and supplier management.
Experience with NLP and IDP techniques: OCR, entity extraction, document classification, and structured data extraction from unstructured AP documents.
Understanding of Graph Databases and Vector Databases (Pinecone, Weaviate, pgvector), and their application to AP knowledge retrieval and semantic search.
Experience with cloud AI services: AWS Bedrock / SageMaker, Azure OpenAI, GCP Vertex AI – for both model inferencing and fine-tuning.
Strong communication and stakeholder management skills; proven ability to present AI concepts and product roadmaps to C-suite audiences.
Experience participating in RFI/RFP responses, thought leadership, and business development in the AI/Finance technology space.
People leadership skills; candidate is hands-on technically while managing data scientists, ML engineers, and product managers.
Working knowledge of statistical and ML concepts: classification, regression, anomaly detection, and time-series analysis applied to finance data.
Short Description-
We are seeking a seasoned Sr. AVP – AI Product Experience to own the design, build, and deployment of AI-powered product capabilities for the Accounts Payable domain. The role demands deep technical grounding in Generative AI, Agentic AI, and LLM engineering, combined with strong product leadership to translate AP process complexity into scalable, intelligent software solutions. The incumbent will work closely with engineering, data science, and client-facing teams to define and ship world-class AI experiences on EXL's Finance AI platform.
Description-
KEY RESPONSIBILITIES
Product Vision & Platform Strategy
Own end-to-end product vision and multi-year roadmap for AI-powered AP solutions: intelligent invoice processing, three-way matching, supplier onboarding, exception management, and payment orchestration.
Define product architecture and feature set for Generative AI and Agentic AI capabilities embedded within AP workflows, ensuring alignment with EXL's Finance AI platform strategy.
Participate in deep architectural discussions and design exercises to create world-class AP AI solutions, ensuring they are designed for successful deployment on cloud platforms (AWS, Azure, GCP).
Drive build-vs-buy-vs-partner decisions, evaluating AI frameworks and vendor ecosystems against in-house capability development.
Solution Design & AI Architecture
Design Generative AI solutions leveraging state-of-the-art models (GPT-4o, Claude, Llama 3) to automate AP-specific tasks such as invoice data extraction, PO matching, and vendor communication.
Architect agentic AI workflows for autonomous AP operations: self-healing exception agents, multi-step approval orchestration agents, and real-time supplier query resolution agents.
Translate complex AP business requirements into AI architectures, orchestration frameworks (LangChain, LangGraph, AutoGen, CrewAI), and scalable deployment pipelines (MLOps, MLflow).
Design NLP and Intelligent Document Processing (IDP) capabilities for structured and unstructured AP documents: invoices, POs, GRNs, contracts, and remittance advices.
Ensure AI solutions meet compliance, security, and scalability standards including SOX, GDPR, and enterprise data governance requirements.
Technical Leadership
Guide technical teams of data scientists, ML engineers, and software engineers in building, fine-tuning, and deploying LLMs and multimodal AI models for AP use cases.
Architect scalable, secure AI pipelines on cloud platforms; drive adoption of deep learning frameworks (PyTorch, TensorFlow) and LLM orchestration stacks (LangChain, LlamaIndex, LangGraph).
Stay current on advancements in Generative AI, agentic systems, NLP, and ML engineering; translate emerging trends into product features and competitive differentiation.
Drive engineering excellence: code quality standards, MLOps practices, model versioning, and production monitoring across AP AI models.
Client Engagement & Go-to-Market
Interact directly with client CTO, CPO, and Finance technology leaders to understand AP pain points, showcase EXL's AI capabilities, and co-create transformation roadmaps.
Lead client solutioning during first engagements: define scope, architect proof-of-concepts, and present AI use cases to both technical and business audiences.
Author and contribute to EXL customer-facing publications: whitepapers, workshops, demo environments, and proof-of-concept accelerators for AP AI.
Represent EXL at industry forums, analyst briefings (Gartner, Forrester), and RFP/RFI responses as the Finance AI product SME.
Build deep relationships with senior technical stakeholders at client organizations, enabling them to become AI advocates internally.
Product Management & Delivery
Define and maintain a prioritized product backlog with detailed PRDs, user stories, and acceptance criteria aligned to AP domain outcomes.
Establish product KPIs: straight-through processing (STP) rates, invoice auto-match accuracy, exception reduction, and cycle time improvements.
Capture and share best-practice knowledge across EXL's AI product and solutions architect communities.
Mentor a cross-functional team of product managers, solution architects, ML engineers, and finance SMEs.
QUALIFICATIONS
Education & Experience (14+ Years)
B.Tech or M.Tech in Computer Science, Software Engineering, Data Science, or related technical discipline.
14–16 years of total experience, with at least 6–8 years in AI/ML product engineering and 8+ years in Finance technology with deep AP/P2P domain ownership.
8+ years of hands-on experience in Generative AI and Large Language Models – finetuning LLMs, prompt engineering, and building with LLM orchestration frameworks (LangChain, LlamaIndex, RAGAS, LangGraph).
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) as evidenced by production deployments or public repositories.
Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine-tuning for finance-specific models.
Strong experience in Agentic AI: Autonomous Agents, AutoGen, CrewAI, LangGraph workflow design; familiarity with NVIDIA AI Blueprints and Google AI Agents.
Experience in ML Engineering and MLOps, including MLflow, model versioning, drift monitoring, and retraining pipelines.
Proven ability to deliver complex AI products from ideation through production; track record of driving go-to-market for enterprise AI solutions.
Strong software engineering skills in Python, with experience building and integrating AI APIs, microservices, and cloud-native architectures.
Skills & Competencies
Deep functional knowledge of Accounts Payable: P2P lifecycle, ERP integrations (SAP S/4HANA, Oracle Fusion, Coupa, Ariba), invoice automation, and supplier management.
Experience with NLP and IDP techniques: OCR, entity extraction, document classification, and structured data extraction from unstructured AP documents.
Understanding of Graph Databases and Vector Databases (Pinecone, Weaviate, pgvector), and their application to AP knowledge retrieval and semantic search.
Experience with cloud AI services: AWS Bedrock / SageMaker, Azure OpenAI, GCP Vertex AI – for both model inferencing and fine-tuning.
Strong communication and stakeholder management skills; proven ability to present AI concepts and product roadmaps to C-suite audiences.
Experience participating in RFI/RFP responses, thought leadership, and business development in the AI/Finance technology space.
People leadership skills; candidate is hands-on technically while managing data scientists, ML engineers, and product managers.
Working knowledge of statistical and ML concepts: classification, regression, anomaly detection, and time-series analysis applied to finance data.
QUALIFICATIONS
Education & Experience (14+ Years)
B.Tech or M.Tech in Computer Science, Software Engineering, Data Science, or related technical discipline.
14–16 years of total experience, with at least 6–8 years in AI/ML product engineering and 8+ years in Finance technology with deep AP/P2P domain ownership.
8+ years of hands-on experience in Generative AI and Large Language Models – finetuning LLMs, prompt engineering, and building with LLM orchestration frameworks (LangChain, LlamaIndex, RAGAS, LangGraph).
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow) as evidenced by production deployments or public repositories.
Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine-tuning for finance-specific models.
Strong experience in Agentic AI: Autonomous Agents, AutoGen, CrewAI, LangGraph workflow design; familiarity with NVIDIA AI Blueprints and Google AI Agents.
Experience in ML Engineering and MLOps, including MLflow, model versioning, drift monitoring, and retraining pipelines.
Proven ability to deliver complex AI products from ideation through production; track record of driving go-to-market for enterprise AI solutions.
Strong software engineering skills in Python, with experience building and integrating AI APIs, microservices, and cloud-native architectures.
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