Key Outcomes & Responsibilities ● AI Strategy & Roadmap: Define the technical AI vision for the ECM portfolio, focusing on the transition from traditional OCR/Extraction to LLM-powered cognitive understanding and Agentic AI for document-heavy workflows. ● Architect multi‑tenant, cost‑efficient GenAI services (prompt orchestration, retrieval‑augmented generation, evaluation harnesses, guardrails) consumable across WorkDesk, IDP, and Content Management surfaces. ● Advance document intelligence: combine LLMs with layout understanding, OCR/ICR/OMR/MICR, and computer vision; expand pre‑trained templates and Model Training Studio for continuous learning on new document types. ● Ship next‑gen enterprise search (semantic + hybrid vector), relevance tuning, and cross‑repository federation; enable context‑aware retrieval in ECM workflows. ● Governance & Ethical AI: Establish frameworks for "Explainable AI" to ensure that automated decisions within the ECM platform are auditable, transparent, and compliant with global data privacy regulations (GDPR, SOC2, etc.). ● Establish robust MLOps: data curation/labelling, training/validation, bias & safety testing, model registry, blue/green and canary rollouts, telemetry, and cost governance across clouds. ● Build, mentor and lead a high‑performing team (applied scientists, ML engineers, platform engineers, data/ops, evaluation & safety) with strong engineering and scientific rigor. ### Requirements ● 10+ years of experience in AI/ML with a demonstrable track record of shipping production AI; leadership experience managing AI/ML engineering teams. ● Depth in document & language AI: LLMs (prompting, fine‑tuning, RAG), information retrieval (BM25, vector search, hybrid ranking), computer vision for documents, and OCR/ICR/OMR/MICR. ● Architecture & MLOps: multi‑tenant AI services, feature stores, model registries, CI/CD for ML, observability, and cost/performance optimization on Azure/AWS/GCP. ● Stakeholder leadership: partner with Product Management and GTM teams to define outcomes and articulate AI tradeoffs to customers, executives, and analysts. ● Excellent communication and storytelling skills for technical and non‑technical audiences. Indicative Tech Stack : AI/ML: PyTorch/TensorFlow, Hugging Face, LangChain/LlamaIndex, ONNX/Triton; IR/Search: Elastic/OpenSearch + Vector DB (FAISS/Pinecone/Weaviate); Pipelines & MLOps: Airflow/Kubeflow/MLflow, Feast/feature store, Docker/K8s; Data: Lakehouse (Delta/Iceberg), OCR engines; Cloud: Azure/AWS/GCP; Observability: Prometheus/Grafana.