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Leads the engineering organization building reliable, governed AI execution platforms for regulated banking customers. Owns engineering strategy, high-availability workflows, deployment within bank infrastructure, multi-LLM governance, auditability, observability, incident response, and regulatory compliance. Builds cross-functional teams, hires senior talent, translates evolving requirements into roadmaps, and represents the company with customers and regulators.

Lead the design and production implementation of AI research systems that transform enterprise knowledge into governed, provenance-rich ontologies. Build grounded extraction, knowledge graphs, retrieval-generation pipelines, model adaptation loops, reinforcement-learning environments, learning infrastructure, and rigorous evaluation frameworks. Ensure outputs are evidence-backed, safe, observable, and optimized for quality, latency, and cost in regulated enterprise environments.

Build and own production CI/CD pipelines with automated security gates, policy-as-code enforcement, secrets rotation, vulnerability auto-remediation, zero-downtime deployment, rollback, and self-healing infrastructure. Improve developer feedback loops, deployment observability, incident recovery, and compliance enforcement across build and runtime environments. The role requires strong expertise with Git, CI/CD platforms, Docker, Kubernetes, infrastructure-as-code, security controls, and automated remediation.

Build production-grade AI systems that convert enterprise documents, interviews, and system logs into governed process ontologies. Responsibilities include document ingestion, schema-constrained LLM extraction, retrieval and reranking, knowledge graphs, entity resolution, GraphRAG, grounding, multi-agent orchestration, MCP/A2A integrations, and evaluation frameworks. The role requires close collaboration with product and research teams to deliver evidence-backed, reliable AI capabilities for regulated enterprise environments.

Leads strategic HR initiatives across the employee lifecycle, including AI-enabled people systems, HRIS governance, people analytics, performance management, engagement, employer branding, and early-career talent programs. Partners with senior leaders and technology teams to build an AI-native culture, improve data-driven HR operations, ensure regulatory compliance, and translate employee insights into organizational action. Mentors HR partners and business leaders on equitable, effective people processes.

As Principal Engineer, AI Research, you will lead AI systems development focusing on reliability and governance in enterprise environments, translating AI strategy into production systems, solving complex AI reliability problems, and mentoring teams.

As a Senior Software Engineer, you will design and implement workflow systems, build orchestration layers, and mentor junior engineers while ensuring reliable and observable AI workflows.

The Principal, Forward Deployed Engineering will lead technical customer engagement, manage enterprise deployments, define use cases, and drive adoption of the kAIgentic platform across complex environments.

As a Principal Product Manager, you will convert strategic signals into product direction, manage execution discipline, and enhance customer value through AI-powered enterprise solutions.

Drive the infrastructure strategy for AI platform, leading a DevOps team for critical systems in regulated environments, focusing on cloud, Kubernetes, observability, and security operations.