Jobs at kAIgentic

Lead security architecture and compliance for an enterprise AI platform serving regulated banking customers. Responsibilities include designing tenant isolation, encryption and key management, agentic AI security controls, compliance-by-design, ISO 27001/42001 and SOC 2 programs, audit coordination, third-party risk, privacy, resilience planning, customer security due diligence, and secure development practices.

Own an enterprise AI product area from strategy through delivery and adoption while leading experience standards across the platform. Define roadmaps, requirements, UX principles, cross-product journeys, adoption metrics, and human oversight for regulated workflows. Conduct discovery with banking and compliance experts, translate market and regulatory insights into product direction, influence engineering and senior stakeholders, and prototype AI-enabled experiences that reduce cognitive load and build user trust.

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.

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.

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.

Build and maintain durable, observable orchestration for long-running AI workflows. Design Temporal-based workflow systems, LLM orchestration and self-correction, observability pipelines, interrupt-and-resume human-in-the-loop patterns, and own reliability under production load.