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 robust 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.

📌 AI Lead- ECM (India)
🏢 MarketScope
📍 India