The role operates at the intersection of technology strategy, product discovery, domain‑driven design, and engineering execution, driving rapid experimentation, applied innovation, and technical excellence while respecting real‑world operational, security, and regulatory constraints. Success is measured by validated learning, speed‑to‑insight, and successful transition of ideas into core product organizations. The role is expected to be hands-on with 40% time dedicated to Software Development and the rest on the remaining responsibilities.
Key Responsibilities
Innovation & Idea Incubation Leadership
- Own the end‑to‑end lifecycle of idea incubation from problem discovery and hypothesis framing to prototyping, validation, and graduation or termination.
- Establish repeatable incubation frameworks using design thinking, Lean Startup, and experiment‑driven development.
- Champion a culture of experimentation, learning velocity, and evidence‑based decision‑making in high‑ambiguity environments.
- Ensure early‑stage experimentation incorporates domain realities from Buildings, Security, and Industrial contexts, avoiding innovation that cannot transition to real deployments.
Technical & Architectural Leadership
- Provide technical direction across incubated initiatives, ensuring solutions are scalable, secure, resilient, and production‑ready.
- Guide teams in selecting and applying emerging technologies including cloud‑native platforms, SaaS, AI/ML, GenAI, IoT, and edge computing.
- Ensure architectural decisions explicitly address non‑functional requirements such as availability, scalability, security, compliance, and operational resilience.
- Partner with Architecture and SRE leaders to ensure smooth transition of incubated solutions into enterprise‑scale delivery models and long‑lived product platforms.
AI SDLC + SW Engineering Skills
End-to-End AI Lifecycle : Data → model → deployment → monitoring
MLOps / LLMOps + CI/CD : Pipelines, versioning, automated deploy & testing
Data & Model Engineering: Data pipelines + LLMs/RAG + model optimization
Software Engineering Fundamentals:
- API design, microservices
- Clean code, testing
- System design
Cloud-native Architecture: Kubernetes, Docker, scalable AI services
**Domain‑Driven Engineering Leadership **
Ensure engineering teams deeply understand and design for domain‑specific constraints and requirements, including:
- Buildings (commercial buildings, campuses, multi‑site operations, 24×7 uptime expectations)
- Security (cybersecurity, privacy, regulatory compliance, secure‑by‑design principles)
- Industrial / OT (manufacturing, process industries, legacy systems, safety‑critical environments)
Drive domain‑driven design practices so incubated solutions reflect real operational workflows, asset models, and system boundaries.
Validate that domain requirements are explicitly captured and tested during incubation, not deferred to later product teams.
Buildings Domain Knowledge
- Has led engineering innovation for software platforms that integrate with Building Management Systems (BMS) such as HVAC, lighting, energy, fire, and life‑safety systems.
- Ensure incubated solutions support multi‑site and multi‑tenant building deployments and can scale reliably beyond pilot environments.
- Drive interoperability across heterogeneous OT devices, protocols, and vendor ecosystems common in building environments.
- Ensure experimentation and pilots account for continuous operations and minimal disruption expectations in live buildings.
Security & Privacy Knowledge
Own security‑by‑design and privacy‑by‑design accountability across all incubated software initiatives.
Ensure security requirements align with industrial and OT security standards (e.g., ANSI/ISA‑62443) and enterprise security policies.
Require and oversee:
- Threat modeling and secure architecture reviews
- Strong authentication, authorization, and role‑based access control
- Secure handling of credentials, secrets, and certificates
- Audit logging and traceability for security‑relevant actions
Ensure compliance with data protection and privacy regulations (e.g., GDPR) and completion of required Privacy Impact Assessments (PIA) prior to graduation of incubated solutions.
Partner closely with Security, Legal, and Compliance teams to ensure incubated ideas are viable in regulated environments.
Industrial & OT Engineering Knowledge
- Led incubation of software solutions for industrial and OT‑centric environments, including manufacturing, energy, and process industries.
- Ensure platforms can integrate with industrial control systems (ICS) such as SCADA, PLCs, edge devices, and legacy systems.
- Drive architectures that support deterministic performance, high‑frequency telemetry, and near‑real‑time decision support where required.
- Balance innovation speed with production continuity, safety, and reliability expectations in live industrial environments.
- Ensure solutions are designed for long asset lifecycles, staged rollouts, and controlled upgrade windows typical of industrial customers.
Organization & Talent Leadership
- Build, mentor, and retain high‑performing engineering leaders and senior technologists.
- Develop engineering managers and tech leads capable of balancing experimentation speed with technical rigor and domain responsibility.
- Foster an inclusive, high‑accountability culture emphasizing ownership, craftsmanship, and engineering judgment.
Business & Stakeholder Partnership
- Collaborate with Product, UX, Data, OT, and Business leaders to align incubated ideas with strategic priorities and domain realities.
- Translate technical and domain insights into compelling narratives for senior leadership.
- Serve as a trusted advisor on innovation strategy, platform evolution, and technology investment decisions.
Execution & Governance
- Define success metrics for incubation including validated learning, domain readiness, and transition viability.
- Ensure disciplined execution through lightweight governance, clear decision points, and risk transparency.
- Own budget, capacity planning, and vendor strategy for innovation initiatives.
Preferred Experience & Skills
- Experience with Buildings, Industrial, SaaS, or platform‑based businesses.
- Exposure to OT environments, cybersecurity, or regulated systems.
- Experience with AI/ML, GenAI, data platforms, IoT, or edge ecosystems.
- Familiarity with Lean Startup and design thinking methodologies.
- Exceptional ability to operate under ambiguity and drive clarity through experimentation.
- Strong storytelling, influence, and executive communication skills.