The Role
Lead an engineering team to define and deliver AI for BPM: build agentic AI for non‑deterministic workflow steps, productionize process insights and simulations, embed RPA, implement MLOps/LLMOps across clouds, enforce model/data/prompt governance, and recruit and mentor a multidisciplinary team while interfacing with product, customers, and partners.
Summary Generated by Built In
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
Skills Required
- 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 and language AI: LLMs (prompting, fine-tuning, RAG), program synthesis, IR/vector search, time-series forecasting
- Familiarity with OCR/IDP
- BPM foundations: hands-on experience with BPMN, DMN, CMMN, case management, exception handling, and event-driven microservices
- RPA and hybrid orchestration: embedding bots in processes with policy controls and exception/case handling patterns
- MLOps/platform experience: multi-tenant AI service design, model registries, CI/CD for ML, telemetry/observability, evaluation suites, cost/performance optimization on major clouds
- Governance and security: data privacy, PII handling, audit trails, model/prompt guardrails and bias/safety evaluation
- Regulated environment experience
- Stakeholder leadership: partner with Product, GTM, Customer Success and communicate trade-offs to executives and customers
- Recruit, mentor, and grow a multi-disciplinary engineering team and represent BPM AI with customers, partners, and analysts
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The Company
What We Do
Marketscope is a technology company specializing in the development and integration of Advanced Driver Assistance Systems (ADAS) and the scaling of production-grade AI/ML applications. The company focuses on AI platform engineering and product stacks, targeting strategic enterprise accounts and government sales, particularly within the Indian market, while expanding its reach into new international industries.
