Role Description Precisely is hiring a Director of AI Engineering to lead the design, development, and deployment of internal enterprise AI solutions that drive business transformation and operational efficiency. This is a hands-on technical leadership role responsible for building AI systems, setting engineering standards, and leading a team of AI engineers. The role spans the Microsoft Copilot Studio, Azure AI Foundry, and Claude Enterprise ecosystems, delivering AI-based employee experiences, and automating and augmenting workflows that reduce cost, accelerate cycle times, and enable revenue. - Design and operationalize agentic AI systems with multi-step workflows, orchestration frameworks, and human-in-the-loop patterns. - Architect and build AI systems including agentic workflows, RAG pipelines, evaluation harnesses, orchestration layers, and prompt engineering. - Prototype new AI patterns and capabilities to stay ahead of the rapidly evolving landscape. - Set technical standards through code reviews, design documentation, and reference implementations. - Hire, mentor, and develop an agile team of AI engineers. - Represent AI engineering in executive discussions, vendor partnerships (Microsoft, Anthropic), and cross-functional planning. - Define data requirements for AI models and validate pipeline quality, governance, and freshness. - Coordinate with Enterprise Applications to integrate AI capabilities into existing business systems (Salesforce, NetSuite, Workato) without creating parallel systems or integration debt. - Prioritize AI use cases that enhance enterprise workflows through intelligent automation, predictive insights, and embedded copilot experiences. - Align on shared data models, integration patterns, and change management processes. Qualifications - 10+ years in software engineering with current, deep technical fluency and active production coding. - 2+ years leading or mentoring engineering teams (formal management or tech-lead experience). - Daily use of AI-assisted developer tools (GitHub Copilot, Claude Code, Cursor, or equivalent). - Strong architecture skills in cloud-native AI on Microsoft Azure (Azure AI Services, Azure OpenAI, Entra ID, Microsoft Graph). - Demonstrated ability to co-architect across platform, data, and enterprise application teams. - Track record of translating ambiguous business problems into shipped AI solutions. - Experience establishing AI governance, evaluation, and quality frameworks. - Ability to communicate complex technical concepts to executive and non-technical audiences. Preferred Skills - Background in data integration, data quality, or master data management. - Experience with enterprise application ecosystems (Salesforce, NetSuite, Workato, ServiceNow). - Knowledge of AI regulatory frameworks (NIST AI RMF, EU AI Act, ISO 42001). Benefits - This position is 100% remote anywhere in the US.

• Define and own end-to-end technical architecture for a dual-mode agentic AI system across offline (edge) and cloud-connected (AWS Bedrock) environments • Drive architectural decision-making across agentic orchestration, data synchronization, voice and chat interfaces, and cloud infrastructure; author and maintain Architecture Decision Records (ADRs) • Act as the primary technical authority within the Engineering POD and the key technical interface with client stakeholders • Bring an AI-forward development practice to every phase of the engagement — using tools like Claude, Cursor, and agentic coding assistants as force multipliers across implementation, code review, and technical documentation • Guide and align the engineering team across AI/ML, full stack, and integration workstreams • Take direct implementation ownership across cloud infrastructure provisioning, local SQLite data layer, cloud data mirror, STT/TTS pipeline integration, and REST API surface • Build and configure the AWS environment end-to-end — including Bedrock, Guardrails, storage, and connectivity services • Implement the edge-to-cloud data synchronization strategy between offline local storage and AWS-hosted data stores • Contribute to agentic orchestration implementation, integration testing, and iterative deployment; support incoming team members with onboarding and context transfer • Design the agentic orchestration layer with tool-use, contextual memory, and conversational interaction patterns for both offline and connected modes • Select an edge LLM based on prior experience with comparable hardware profiles; configure AWS Bedrock with Guardrails for connected-mode reasoning and content safety • Collaborate with the AI/ML Engineer on prompt engineering, model evaluation, and proactive recommendation logic • Ensure architectural consistency and integration quality across voice (STT/TTS) and chat modalities on a shared backend • Provide technical quality assurance and architectural governance across all POD workstreams • Lead cross-functional engineering coordination across AI/ML, full stack, product, and UX tracks • Drive iterative delivery cadence — shipping testable software at each phase review checkpoint and supporting investor-facing demonstrations and POC readouts • Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace.

• Design, implement, and maintain Python-based services and workflows that integrate LLMs and GenAI capabilities with internal platforms and applications. ** • Build and iterate on agentic and multi-step workflows using approved orchestration frameworks and platform patterns (e.g., LangGraph, AgentCore, LangChain). ** • Consume existing retrieval/RAG and search abstractions to improve response quality, grounding, and reliability, tuning parameters (top-k, scoring, filters) rather than re-implementing core retrieval infrastructure. ** • Develop robust tooling and APIs for agents, including clear input/output schemas, error contracts, versioning, and observability hooks. ** • Implement structured output handling (e.g., JSON schemas, tool calls) to ensure predictable behavior and easier downstream integration. • Operate within established platform, security, and governance guardrails (RBAC, data access boundaries, PII handling, logging, audit) instead of building custom, one-off mechanisms. ** • Leverage existing platform SDKs, templates, and patterns for configuration, deployment, and monitoring of GenAI workloads. ** • Work in a cloud-native AWS and Azure environments (e.g., cloud native applications, environment variables, secrets management, logging/metrics/tracing), collaborating with platform teams as needed rather than owning core infra design. • Partner with product managers, internal business stakeholders, and UX to translate problem statements and evaluation criteria into concrete, production-ready workflows. ** • Collaborate with data and application teams to integrate GenAI capabilities into existing systems (e.g., internal tools, portals, automation flows) with minimal disruption. ** • Participate in design reviews, code reviews, and architecture discussions, ensuring solutions are maintainable, observable, and aligned with platform standards. ** • Contribute to internal enablement (playbooks, examples, patterns) to help eTech Engineering and other teams become effective consumers of GenAI capabilities. • Own the operational health of GenAI workflows you build: monitoring, alerting, troubleshooting, and iterative improvement. ** • Incorporate evaluation and guardrail checks (e.g., automated tests, evaluation harnesses, red/blue team feedback) into workflows to improve quality and safety over time. ** • Act as a “high adopter of AI to build AI”, continuously using AI tools (e.g., Glean, Devin, Windsurf, Claude) to accelerate design, development, testing, and documentation.

• Design the agentic systems that run across hospitality design, hospitality strategy, and marketing workflows • Decide the stack: which tools, which agents, which integrations, what memory architecture, what evaluation harness • Document the architecture so it compounds rather than evaporates when you take leave • Build agents using Claude, ChatGPT, Make, n8n, Zapier, the Claude Agent SDK, and adjacent platforms • Maintain integrations across our stack: Gmail, Google Workspace, Streak, Xero, WhatsApp, ArchiCAD outputs, WordPress, Meta Ads, Google Ads, LinkedIn, Klaviyo • Build evaluation and observability into every agent. We need to know when one fails. • Build AI-assisted content systems for LinkedIn, Instagram, blog, EDM and short-form video, calibrated to brand voice per client • Architect the brand voice infrastructure: founder interviews to voice guide to enforcement layer across all content output • Build performance reporting pipelines that pull Meta Ads, Google Ads, LinkedIn Ads, and organic data into client-ready insights • Design ad creative generation and variation pipelines tied to performance feedback loops • Build SEO and AI-search optimisation systems (GEO / AEO), including keyword clustering, content briefs, on-page optimisation, and schema markup • Build lead intake systems across website, WhatsApp, referral and inbound email, with classification and routing • Design qualification scoring against our ICP, and the agent layer that drafts initial follow-ups • Architect the CRM rhythm in Streak: deal stage updates from meeting transcripts, proposal follow-up cadence, pipeline reporting • Build fee proposal generation: brief in, branded proposal out, with version control and follow-up logic • Design LinkedIn lead-scraping, enrichment, and personalised outreach workflows for BDM • Build the trigger logic for which deals sees personally and which the system handles • Build the project workspace generation that triggers when a deal is won: folder structure, templates, access, programme, kickoff brief • Design the reverse brief drafting agent that converts kickoff notes into structured project briefs • Architect the cost planning workflow: from sketch to v0 cost plan in our FOH/BOH/Services/FFE/Prelims structure, with builder benchmark lookups • Build the builder budget revision loop: receive revised budget, reconcile against cost plan, draft client memo, schedule meeting • Design RFI log management, drawing register hygiene, and subcontractor follow-up systems • Build the weekly client status reporting layer per active project • Support bookkeeping rhythm with automation: Xero reconciliation triggers, invoice issuance against milestones, debtor follow-up cadence, BAS prep • Embed AI into the daily operating rhythm of three businesses • Coach the team on how to use the systems you build. We're not buying tools, we're buying capability. • Stay across emerging AI tools, agents, frameworks and platform changes • Bring proactive ideas. Test them. Tell us what worked and what didn't. • Identify where AI extends judgement vs replaces it. Defend the line. • AI is integrated into daily workflows across all three businesses in a measurable, defensible way • The founder recovers significant weekly hours through agent-mediated work • Project status reporting, cost plan generation, and client comms run on assisted workflows we can prove the ROI on • Bookkeeping rhythm (reconciliation, invoicing, debtor chase) is supported by automation • No Filter scales content production without proportional headcount growth • We have a documented, defensible AI operating layer that the business owns, not the vendors.