AI Solutions Delivery Lead

Applied Digital Corporation • Dallas, TX

54d

About The Position

We are seeking an AI Solutions Delivery Lead to architect and implement a new operating paradigm powered by AI. This role focuses not on evaluating individual software applications, but on reimagining how work gets done, designing AI‑enhanced environments, workflows, and processes that materially improve efficiency, accuracy, and cycle time across the organization.

As the connective tissue between business teams, data/engineering groups, and enterprise platforms, you will identify friction points and redesign end‑to-end work patterns using AI agents, LLM‑driven automation, and intelligent decision workflows. You will translate ambiguous operational problems into clear AI-enabled solutions, then lead the build, deployment, and scaled rollout of production-grade AI capabilities.

The ideal leader blends product strategy, delivery rigor, and hands-on fluency in modern AI technologies. You will own the full lifecycle from discovery and governance readiness to solution design, automation architecture, deployment, change management, and adoption ensuring that new AI-powered ways of working are secure, reliable, and tied to measurable business outcomes. This role is instrumental in shaping the organization’s AI-enabled future and embedding AI as a core part of our operational fabric.

Requirements

• Bachelor’s degree in computer science, Engineering, Information Systems, or related field; or equivalent experience.
• 10+ years in technology delivery, product, or solutions architecture roles, including 6+ years leading AI/automation initiatives in enterprise environments.
• Demonstrated track record of shipping production AI solutions (e.g., LLM agents, copilots, intelligent workflows, document/process automation) with measurable business impact.
• Strong understanding of LLM platforms and integration patterns, such as:
• Prompt engineering, tool/function calling, retrieval-augmented generation (RAG), embeddings/vector databases.
• Orchestration frameworks (e.g., Lang Chain, Semantic Kernel, agent frameworks) and workflow automation (e.g., Power Automate, UiPath, ServiceNow, Zapier for prototyping).
• API and event-driven integration with enterprise systems; secure secrets management and identity (OAuth, service principals).
• Experience establishing LLM Ops/ML Ops practices: evaluation frameworks, telemetry/observability, CI/CD for prompts and workflows, A/B testing, human-in-the-loop review.
• Proven ability to bridge business and engineering: requirements discovery, scoping, solution design, change management, and stakeholder communication up to the executive level.
• Working knowledge of security, compliance, and data governance for AI (data classification, PII/PHI handling, access controls, model risk management).

Nice To Haves

• Industry background in operations-heavy domains (e.g., engineering, construction, infrastructure, manufacturing, field services) with complex workflows and documentation.
• Hands-on experience building RAG pipelines (document parsing, chunking strategies, embeddings tuning, retrieval quality evaluation) and multi-agent workflows.
• Familiarity with enterprise AI platforms (e.g., Azure OpenAI Service, Microsoft Copilot Studio, AWS Bedrock, Google Vertex AI), and vector stores (e.g., Azure AI Search, Pinecone, Weaviate).
• Experience with governed AI in the enterprise: policy-as-code, content filtering, model access control, prompt/response logging, and data retention policies.
• Demonstrated vendor management and SI/partner coordination for AI implementations; ability to craft SOWs and value-based acceptance criteria.
• Proficiency with modern data stacks (APIs, ETL/ELT, data lakes, event buses) and at least one programming/scripting language (e.g., Python, TypeScript) for prototypes.
• Strong measurement discipline: defining KPIs, setting baselines, establishing dashboards, and quantifying ROI.
• Master’s degree in a technical or business field (e.g., CS, Data Science, Engineering, MBA) is a plus.

Responsibilities

• Build and maintain an AI portfolio aligned to business value; prioritize based on ROI, feasibility, risk, and time-to-impact.
• Conduct gap assessments across processes and tech stacks to identify where AI/automation can reduce friction and improve KPIs.
• Define success criteria and performance baselines; establish measurable outcomes for every initiative (e.g., cycle time, accuracy, throughput, cost per transaction).
• Lead end-to-end delivery of AI agents, copilots, and LLM-powered workflow automations using commercial platforms and custom development.
• Oversee solution architectures (RAG, tool-use, function calling, agent frameworks) and integrations with enterprise systems (e.g., ERP, CRM, data lakes, document repositories).
• Guide LLM Ops/ML Ops practices: prompt/version management, evaluation harnesses, offline/online testing, monitoring, guardrails, and continuous improvement.
• Ensure production readiness: scalability, reliability, observability, SLOs/SLAs, rollback strategies, and runbooks.
• Design, build, and maintain secure API integrations with leading AI platforms such as Anthropic's Claude, OpenAI, and cloud-native AI services to power internal solutions that operate directly on enterprise data.
• This includes architecting RESTful and event-driven pipelines that connect LLM capabilities to core business systems (ERP, CRM, document repositories, data lakes), enforcing strict data residency and access controls so that sensitive corporate information never leaves approved environments.
• The role will establish standardized integration patterns, including authentication via OAuth and service principals, token management, rate-limit handling, and response caching to enable teams across the organization to rapidly deploy AI-powered agents, copilots, and automated workflows against proprietary datasets at scale, while maintaining full auditability, cost governance, and compliance with internal security policies.
• Partner with Security & Legal to operationalize safe AI (PII handling, data residency, model risk management, approval workflows).
• Implement safety/guardrail layers (policy enforcement, red-teaming, output filtering, hallucination control, human-in-the-loop).
• Drive model selection/evaluation (commercial/open-source) against cost, accuracy, latency, security, and alignment with enterprise standards.
• Drive user onboarding, training, and enablement; establish adoption metrics and feedback loops.
• Create operational playbooks and knowledge assets to scale delivery across teams.
• Manage vendor/partner relationships; evaluate platforms and negotiate outcomes tied to value realization.
• Lead a cross-functional delivery team (solution engineers, prompt engineers, analysts, product owners).
• Communicate roadmaps, risks, and outcomes to executive stakeholders; publish quarterly impact reports and case studies.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 11-50 employees