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We are seeking a Platform Engineering Group Lead to strengthen our platform and operational engineering capabilities and ensure the company has a secure, reliable, and scalable foundation for product delivery and operations. This role drives execution, raises engineering standards, and improves consistency across deployment, infrastructure, and operational practices in support of product delivery, customer environments, and internal platforms. The role partners closely with R&D, QA, IT stakeholders, and PS/CS to enable consistent deployments through standards, reference architectures, and high-quality installation documentation.



Your key responsibilities will be:

Lead the Platform Engineering group, including people leadership, prioritization, execution management, and cross-functional coordination, and establish an effective operating cadence for a small, high-impact organization.
Build scalable operating models, ownership boundaries, and execution discipline across platform, deployment, and operational engineering activities.
Drive automation and deployment engineering outcomes across CI/CD, release readiness, environment provisioning, and delivery tooling, with a strong focus on reducing manual steps and operational risk.
Own deployment readiness across internal and customer-facing environments, including staging deployments, validation steps, installation packages, and deployment runbooks, to support consistent and repeatable implementations.
Ensure effective internal infrastructure and workplace technology operations, including service levels, escalation paths, operational hygiene, endpoint lifecycle, backups, and core office/server-room infrastructure.
Drive security and compliance readiness within the groups scope, including operational controls implementation, remediation tracking, evidence discipline, and coordination with external cybersecurity partners and internal security stakeholders.
Establish engineering standards and technical guardrails across infrastructure-as-code, change management, observability, access controls, and backup/restore verification.
Lead incident management across platform, infrastructure, and internal technology services, including issue assessment, prioritization, escalation, incident reviews, and corrective actions to improve reliability over time.
Define and improve deployment patterns, reference architectures, and environment recommendations based on customer constraints and operational requirements to support successful PS/CS engagements.

We are looking for a Senior AI Prompt Engineer who will own the design, development, and optimization of AI Agent experiences built on the Zowie AI platform. You will engineer the prompts, system instructions, guardrails, and multi-turn conversational flows that power our customer-facing AI Agents across chat and email-automation channels.
This is not a surface-level content role - you will operate at the intersection of language, logic, and AI behavior, shaping how our agents reason, respond, escalate, and self-correct. As part of the Digital & AI team, you will collaborate closely with Product, Engineering, AI/ML, Analysts, CX, Operations, and Localization teams to deliver intelligent, scalable, and trustworthy conversational solutions.
What you'll do:
Design, write, and optimize AI-driven conversational experiences, including system prompts, guardrails, tool-use instructions, and multi-turn flows across chatbot and email-automation channels.
Engineer and maintain reusable prompt frameworks, templates, and conversation patterns that ensure consistency in tone, safety, domain accuracy, and localization across markets on multiple channels such as AI Chat, Ai email bot, AI Voice bot.
Define and refine AI Agent behavior across user scenarios, edge cases, error states, escalation paths, and regulatory/compliance requirements.
Own end-to-end conversational journeys - from problem discovery and use-case research through design, prompt engineering, testing, deployment, and iterative optimization.
Build and maintain prompt evaluation pipelines - designing test cases, scoring rubrics, and regression tests to systematically measure prompt quality, hallucination rates, and task-completion accuracy.
Monitor, analyze, and improve AI Agent performance using analytics dashboards, QA outputs, hallucination findings, user feedback, and operational metrics; translate insights into concrete prompt improvements.
Collaborate cross-functional with Product, Engineering, AI/ML, CX, and Operations teams to identify high-impact use cases, define agent capabilities, and deliver scalable solutions.
Contribute to internal prompt engineering guidelines, conversational design systems, and AI best practices - helping establish our standards for responsible, effective AI Agent deployment.
Stay current with advancements in LLMs, agentic AI patterns, prompt optimization strategies, and conversational AI tooling; bring relevant innovations into the teams workflow.

Required Senior AI Engineer - Applied AI Engineering Group
The Dream Job
It starts with you - an engineer driven to build the agentic AI platform that turns LLMs into reliable, production-grade capabilities. You care about clean APIs, well-defined service boundaries, and systems that teams can build on with confidence. Dream is AI-first across the board - every team builds and operates agents. You'll architect and ship the platform that makes this possible: agent orchestration frameworks, LLM gateways, evaluation pipelines, tool-calling infrastructure, and retrieval systems. Without this platform, agents don't ship - you own the layer that turns AI research into Sovereign AI products, deployed across cloud and on-prem environments.
If you want to make a meaningful impact, join our mission and build the agentic AI platform that drives Sovereign AI products - this role is for you.
The Dream-Maker Responsibilities
Design and build agentic systems - single and multi-agent workflows with planning, memory, context engineering, and tool use - for both internal automation and product-facing autonomous capabilities operating over long time horizons.
Build and operate the AI platform layer - LLM gateways, prompt management, structured output handling, tool-calling infrastructure, and cost/latency optimization - deployed on Kubernetes, consumed by every team for their agentic work.
Own the agent framework layer - orchestration primitives, execution environments, state management, and sandboxed tool execution - giving every team the building blocks to create and operate their own agents.
Build evaluation infrastructure that gives teams confidence in agent behavior - automated LLM and agent evals for quality, correctness, safety, latency, cost, and regressions, including human-in-the-loop oversight for mission-critical workflows.
Productionize and harden backend services (APIs, gRPC, async workers) that integrate LLMs - with proper error handling, retries, circuit breakers, and high-availability patterns.
Own RAG pipelines and retrieval systems - indexing, chunking, embedding, vector database management, filtering, and relevance tuning for production retrieval.
Optimize performance and cost across the AI stack - model routing, caching, batching, and inference cost management.
Ship shared tooling - libraries, SDKs, agent templates, and documentation - while working closely with ML Platform, Data Platform, DevOps, and other teams across the Applied AI Engineering group. Own architecture, documentation, and operations end-to-end.