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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.

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.

Join our companys Research Group and shape the next generation of AI for enterprise CX. As a Lead Data Science Researcher, you will own highimpact research initiatives across NLP, Vision, and multimodal domains, with a strong emphasis on large language models (LLMs) and agenticAI systems.
You will combine deep handson technical work with leadership-setting direction, mentoring peers, and translating breakthrough ideas into reliable, productiongrade capabilities for our companys contact center solutions.
You will collaborate closely with researchers, engineers, product leaders, and subjectmatter experts to define strategy, validate research hypotheses, and lead the transition from experimental agentic systems to reliable, realworld deployments.
How will you make an impact?
Lead end‑to‑end research initiatives across NLP, Vision, and multimodal modeling, with a strong focus on LLM‑based and agentic‑AI systems.
Architect, prototype, and evaluate singleagent and multiagent systems, including planning, tool use, memory, and orchestration.
Establish and own best practices for safe, controllable, and scalable AI agents, including evaluation frameworks, guardrails, fallback strategies, and observability.
Act as a technical authority on LLM and agentic systems, guiding architectural decisions, evaluation strategies, and engineering tradeoffs.
Define rigorous offline and online evaluation strategies (KPIs, A/B testing, cost/performance tradeoffs) grounded in realworld constraints.
Deliver select research components at production quality and partner closely with productization teams to harden and deploy endtoend solutions.
Mentor researchers and data scientists, raising the bar for technical rigor, engineering quality, and applied research impact.
Communicate complex findings and risks clearly to crossfunctional stakeholders and leadership.
Stay at the forefront of AI research, contributing to the teams agentic‑AI roadmap and long‑term research vision and help set teamwide standards and guidelines.