VP of Engineering

The role

Define and lead the engineering organization that delivers reliable, governed AI-driven execution for regulated banking customers. Own the end-to-end reliability, auditability, and multi-LLM governance while the product and requirements continue to evolve. Partner with architects, regulators, and customers to embed the intelligence layer inside bank-owned infrastructure.

What you'll do

• Define the engineering strategy for non-deterministic execution, ensuring five-nine reliability and resumable workflows with human-in-the-loop control.

• Lead the delivery of deployment pipelines that operate inside customer-owned, highly regulated infrastructure, meeting strict security and compliance mandates.

• Own the governance framework for model and LLM decisions, implementing audit trails and multi-LLM policy enforcement across the execution gateway.

• Build cross-functional teams that embed the intelligence layer, aligning product, security, and SRE practices to sustain continuous delivery under shifting requirements.

• Drive the feedback loop from production deployments into the ontology, translating field insights into product enhancements and roadmap inputs.

• Align engineering processes with regulatory audit cycles, establishing observability, incident response, and SLO ownership that satisfy banking auditors.

• Represent kAIgentic to senior customer and regulator stakeholders, articulating technical risk mitigation and continuity plans.

• Build the org’s talent pipeline, hiring senior engineers and establishing career paths that scale with the growing execution platform.

What you'll bring

• Proven expertise leading large-scale AI execution platforms with a track record of delivering high-availability, auditable systems.

• AI-native velocity as a default mode of working

• Deep mastery of distributed workflow orchestration, durable execution engines, and LLM integration, with hands-on experience in Go and Python.

• Extensive knowledge of deployment architectures inside bank-owned data centers, including network segmentation, zero-trust security, and compliance-by-design.

• Strong background in reliability engineering, SLO design, incident management, and building observability for multi-tenant, high-throughput environments.

• Experience shaping governance models for multi-LLM ecosystems, defining policy enforcement, audit logging, and model lifecycle controls.

• Ability to translate ambiguous, evolving product requirements into concrete engineering roadmaps and delivery cadences.

• Strategic judgment to balance speed of AI-native development with rigorous regulatory constraints, communicating clearly with both technical and non-technical audiences.

• Demonstrated skill in building high-performing engineering organizations, hiring senior talent, and fostering a culture of ownership and continuous improvement.