Lead the engineering team that enables citizen developers and enterprises to build autonomous, self-optimizing processes that can reason, decide, and act. Key Outcomes & Responsibilities ● Define the technical AI vision for the BPM portfolio across process design (BPMN/CMMN/DMN), rules intelligence, agentic orchestration, workflow guidance, and continuous optimization using process insights. ● Lead the development of AI Agents that can handle "non-deterministic" steps in a workflow—such as managing exceptions, negotiating outcomes, or dynamically re-routing tasks based on real-time context. ● Advance AI-powered BRMS (DMN, decision tables) for rule discovery, simulation, conflict checks, explainability, and in‑flight edits with safe rollout. ● Productionize process insights & simulations (what‑if, forecasting, best‑action recommendations) with rigorous evaluation against historicals and live telemetry. ● Evolve hybrid human/bot orchestration: embed RPA into end‑to‑end workflows with exception/case handling patterns and diagnostics to identify automation candidates. ● Operationalize agentic AI in workflows using governed agents (data fetch, validations, updates) with auditable runs and policy enforcement. ● Establish model, data, and prompt governance—privacy/PII controls, redaction, tenancy isolation, content filters, prompt/response guardrails, and bias/safety evaluation. ● Build MLOps/LLMOps pipelines for data curation, training/fine‑tuning, RAG retrievals, A/B and canary releases, observability, and cost controls across Azure/AWS/GCP. ● Ensure API‑first services compatible with low‑code DevOps, external systems, and downstream surfaces (web/mobile, portals, WorkDesk). ● Recruit, mentor, and grow a multi‑disciplinary team; represent BPM AI with customers, partners, and analysts. ### Requirements ● 10+ years in AI/ML with 4+ years leading AI/ML engineering teams shipping production AI for workflow/BPM/automation or adjacent domains. ● Depth in process & language AI: LLMs (prompting, fine‑tuning, RAG), program synthesis for models (BPMN/DMN/CMMN), IR/vector search, time‑series forecasting; OCR/IDP familiarity is a plus. ● BPM foundations: hands‑on with BPMN/DMN/CMMN, case management, exception handling, and event‑driven microservices. ● RPA & hybrid orchestration: experience embedding bots in processes with policy controls and exception/case handling. ● MLOps/platform: multi‑tenant AI service design; model registries, CI/CD for ML, telemetry/observability, evaluation suites, cost/perf optimization on major clouds. ● Governance & security: data privacy, PII handling, audit trails, model/prompt guardrails; regulated environment experience preferred. ● Stakeholder leadership: partner with Product/GTM/Customer Success and communicate trade‑offs to executives and customers. Indicative Tech Stack AI/ML: PyTorch/TensorFlow; Hugging Face; LangChain/LlamaIndex; ONNX/Triton; Ray IR/Search: Elastic/OpenSearch + Vector DB (FAISS/Pinecone/Weaviate) Process: BPMN/DMN/CMMN tooling; rules engines; simulators Pipelines/MLOps: Airflow/Kubeflow/MLflow; feature store; Docker/K8s; Grafana/Prometheus Cloud: Azure/AWS/GCP; secrets/key mgmt; policy/guardrail libraries