As Executive Director of our Phygital AI R&D Center, you will drive a bold vision — harnessing IoT, edge-to-cloud computing, and AI to reimagine the employee experience and deliver the workplace of the future.
As a Director of Software Engineering at JPMorganChase within our Workforce Technology team, you will lead a high-performing team of engineers pioneering scalable, real-time AI solutions that turn sensor data into meaningful workplace insights. This is a hands-on leadership role where your technical depth and strategic vision will be equally valued, from architecting resilient edge-to-cloud pipelines to influencing firmwide direction on responsible AI and data governance.
At JPMorganChase, we offer an environment where bold ideas are supported, innovation is expected, and your leadership can create lasting impact. You will operate at the intersection of cutting-edge technology and human-centered design, building a collaborative, inclusive culture where engineers grow, challenges are solved at scale, and phygital AI advancements set the standard for the industry.
Job Responsibilities:
- Drives end-to-end delivery of strategic roadmaps from IoT pilots to production-scale AI implementations, bringing hands-on experience in addressing cybersecurity, scalability, and resilience across edge devices and cloud platforms; architects robust edge-to-cloud pipelines that enable phygital workplace innovation.
- Bridges technical complexity and business impact by drawing on direct, hands-on expertise in IoT and edge-to-cloud architectures to translate technical concepts into clear, actionable value for cross-functional stakeholders and senior leadership.
- Establishes and enforces data governance for IoT-generated data streams, applying FAIR principles (Findability, Accessibility, Interoperability, Reusability) to ensure data quality, traceability, and long-term reusability at scale.
- Champions IoT innovation and AI/ML integration by leveraging hands-on experience removing barriers to scaling deployments and leading efforts to embed AI/ML within IoT ecosystems for real-time, production-grade analytics.
- Sets direction and governance for agentic AI-enabled engineering, including SDLC and TLM automation, to drive measurable improvements in speed, quality, and operational outcomes — spanning AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration — while establishing guardrails for validation, security, resiliency, traceability, and reuse.
- Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives at scale.
- Optimizes the software delivery toolchain through hands-on application of enterprise-authorized AI-assisted development and automation capabilities across the SDLC, maximizing automation value and enabling capacity unlock at scale; expected to dedicate approximately 50% of capacity to active coding and technical contribution, maintaining deep engineering engagement alongside leadership responsibilities.
- Develops engineering talent by mentoring and upskilling teams in IoT data interoperability, edge-to-cloud workflows, and AI model deployment, drawing on practical, hands-on experience to foster a collaborative, inclusive culture grounded in continuous learning.
Required Qualifications, Capabilities, and Skills
- Formal training or certification on software engineering concepts and advanced applied experience
- Hands-On Technical Engagement & Full-Stack Proficiency: Demonstrated ability to implement prototypes and reference code, conduct design and code reviews, and support production issue triage — with an expectation of dedicating approximately 50% of capacity to active coding. Broad hands-on proficiency across Java, Python, and modern front-end frameworks (e.g., React/TypeScript), with strong command of API design and integration patterns for IoT and edge-to-cloud systems.
- IoT Solution Delivery & Edge-to-Cloud Proficiency: Proven track record deploying IoT solutions for operations optimization, predictive maintenance, and real-time monitoring. Deep hands-on knowledge of edge-to-cloud architectures, including sensor integration, data interoperability across fragmented stacks, and low-latency AI inference at the edge (e.g., 5G-enabled processing) transitioning to cloud platforms for advanced ML, big data analytics, and scalable storage.
- AI/IoT Expertise & Technical Depth: Formal training or certification in AI and IoT R&D concepts, combined with advanced applied experience leading technologists to resolve complex technical challenges. Includes expertise integrating IoT data with phygital models, prompt engineering for GenAI on edge devices, and cloud-based model validation.
- Data Governance, Architecture & Resilience: Strong command of FAIR data principles (Findability, Accessibility, Interoperability, Reusability) applied to IoT ecosystems. Demonstrated ability to architect robust, secure edge-to-cloud pipelines that address cybersecurity, cost, and scalability challenges at scale.
- Agentic AI Engineering & Responsible AI Governance: Experience leading adoption of agentic AI-enabled engineering practices using enterprise-authorized tools, including defining human-in-the-loop validation, establishing quality gates, and measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
- Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
- Stakeholder Communication & Influence: Skilled at translating IoT and edge-to-cloud complexity into clear business value for cross-functional teams and senior leadership.
- Transformational People Leadership: Demonstrated ability to lead, inspire, and upskill engineering teams in emerging technologies including IoT, edge computing, and AI, fostering a culture of continuous learning and inclusive collaboration.
Preferred Qualifications, Capabilities, and Skills:
- Advanced degree in Computer Science, AI, or related field
- Certifications in cloud platforms such as AWS IoT or Azure Edge