Agentic AI Platform Engineer

Dine Development Corporation-posted 14 days ago
Full-time • Mid Level
51-100 employees
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As AI rapidly reshapes the consulting landscape by collapsing traditional expertise gaps, accelerating delivery expectations, and rewarding organizations that are both highly specialized and deeply mission aligned, DDC is positioned at the forefront of this evolution by pairing technical innovation with a purpose that serves the Navajo Nation. The AI Engineer will be responsible for designing, developing, and deploying advanced AI solutions on DDC’s Stack AI platform. This hands-on role focuses on building agentic AI workflows as autonomous, multi-step processes powered by large language models (LLMs) that will transform how DDC operates and delivers value to clients. Reporting to the EVP of Innovation, the AI Engineer works within DDC’s AI-First transformation, turning high-level strategy into practical, mission-aligned AI capabilities. By rapidly configuring AI Agents on a modern agentic AI infrastructure, the AI Engineer directly supports DDC’s strategic initiatives, including the AI-First Growth Engine overhaul of our business development processes and the expansion of DDC’s enterprise intelligence capabilities as outlined in the DDC AI-First Strategy. DDC is wholly owned by the Navajo Government and exists to create sustainable and culturally relevant prosperity for the Diné (Navajo People). Because DDC does not answer Wall Street or private equity interests, our Strategic Innovations Team operates with both the responsibility and the freedom to put people and mission first. Our work is inspired by the selfless contributions of the Navajo Code Talkers and reflects a commitment to advance innovation in a way that honors their legacy through service, resilience and meaningful impact.

  • Design and Implement AI Workflows Lead the design and deployment of enterprise-grade AI workflows using the Stack AI platform’s visual builder interface. Construct dynamic pipelines that integrate large language models (LLMs), retrieval mechanisms, enterprise data sources, and multi-step business logic. Architect solutions that address complex tasks such as document summarization, structured content generation, data-driven decision support, and automated process flows. Ensure workflows follow modular design principles, are testable, maintainable, and compatible with DDC’s configuration and deployment lifecycle standards.
  • Develop and Orchestrate Agentic AI Solutions Build autonomous AI agents that perform goal-directed reasoning, access tools and APIs, retrieve contextual information, and coordinate multi-step actions aligned to defined outcomes. Implement robust retrieval-augmented generation (RAG) pipelines to ensure agents can access accurate, relevant, and timely knowledge from across DDC’s structured and unstructured data stores. Design agents that address use cases ranging from proposal assembly to compliance automation, while incorporating fault handling, validation checkpoints, and human-in-the-loop controls to ensure reliability, auditability, and mission alignment.
  • Establish Platform Standards, Patterns, and Design Governance Define and institutionalize platform-wide standards for agent development, including naming conventions, workflow patterns, versioning practices, interface expectations, and logic design rules. Curate and maintain a reference library of approved agentic design patterns and solution blueprints for use across departments. Document prompt and context engineering guidelines, RAG implementation templates, retry strategies, validation frameworks, and ethical decision logic. Promote reusable design across workflows, reducing duplication, increasing maintainability, and ensuring interoperability across agents built within DDC’s Agentic AI Infrastructure.
  • Build and Maintain Reusable Templates, Custom Nodes, and Pro Code Extensions Develop reusable workflow templates that encapsulate proven configurations for common processes and can be rapidly adapted for different contexts. Author reusable custom code nodes using platform supported languages that extend the core functionality of the platform. These pro-code extensions will handle complex operations such as advanced parsing, business-specific logic, API interactions, or data formatting. Package, document, and version these nodes to enable seamless reuse across workflows by both AI engineers and citizen builders, ensuring they are properly governed and integrated into the standards library.
  • Implement Configuration Management, Version Control, and Environment Promotion Establish and enforce configuration management protocols to ensure all workflows, agents, and reusable components are versioned, traceable, and deployable across environments. Define promotion paths from development to staging and production, with clear checkpoints for testing, peer review, security validation, and governance approval. Support rollback procedures, release documentation, and change management to mitigate risk and support rapid iteration without compromising quality.
  • Support Citizen Builders and Enterprise-Wide AI Enablement Serve as a mentor, reviewer, and escalation point for DDC’s community of citizen developers building AI solutions in their functional areas. Provide training, coaching, and design oversight to ensure that citizen-built workflows follow platform standards, respect ethical AI principles, and deliver meaningful value. Develop documentation, internal wikis, and onboarding guides to empower non-technical builders while safeguarding platform integrity. Promote responsible autonomy within the builder community through curated component libraries, sandbox environments, and guided workflow templates.
  • Serve as DDC’s Stack AI Platform Champion and Strategic Liaison Act as the lead point of contact between DDC and Stack AI for all platform engineering matters. Collaborate with Stack AI’s product and technical teams to provide feedback on feature performance, raise platform issues, evaluate new capabilities, and participate in roadmap co-design. Identify emerging integration needs, test beta features, and contribute real-world implementation insights that inform Stack AI’s enterprise enhancements. Represent DDC’s federal use cases and mission-specific priorities in shaping how the Stack AI platform evolves to meet the demands of agentic development at scale.
  • Advance the AI-First Growth Engine and Proposal Transformation Strategy Design and implement intelligent agents that reimagine how DDC identifies opportunities, evaluates fit, assembles pursuit teams, writes proposals, and drives capture operations. Build tools that support knowledge reuse, automated content generation, resume tagging, compliance matrix generation, and competitor analysis. Collaborate closely with growth leaders to align agentic capabilities with pipeline objectives, proposal timelines, and opportunity strategy. Quantify improvements in velocity, win rates, and cost-efficiency tied to AI enablement across the growth engine.
  • Address Enterprise Architecture Challenges and Improve Content Readiness Engage deeply with DDC’s broader enterprise architecture environment including business process design, data architecture, application integration, and infrastructure architecture. Navigate technical debt and fragmentation, especially in systems such as SharePoint where document libraries are siloed and business processes are inconsistently applied. Implement minimum viable products (MVPs) that include workaround strategies when necessary but include forward-looking requirements and recommendations for department heads and functional leads to refactor and improve enterprise content exposure. Drive alignment between agent expectations and content quality, ensuring that both human collaborators and AI agents can efficiently access, interpret, and act upon enterprise knowledge.
  • Develop Agentic Integrations Across Core DDC Systems Design and implement model-to-application interactions that integrate Stack AI agents with core enterprise platforms including SharePoint, Outlook, Teams, ServiceNow, and Workday. Use the Stack AI platform’s supported integrations to enable agents to read and write data, automate ticket creation, retrieve HR policies, query calendars, and interact with collaborative content. Establish repeatable connection patterns, access control rules, data normalization strategies, and fallback handling to ensure secure, efficient tool usage across critical enterprise systems.
  • Expand and Operationalize DDC’s Intelligence Ecosystem Continuously broaden the scope and fidelity of DDC’s AI-accessible knowledge graph. Ingest, classify, and index new sources of structured and unstructured data including resumes, policy documents, program artifacts, proposals, process maps, and systems documentation. Develop and refine metadata tagging strategies and enrichment pipelines to enhance agent retrieval capabilities and contextual accuracy. Promote alignment between business domains and data organization practices to strengthen the foundation of the enterprise intelligence ecosystem.
  • Collaborate Across the Enterprise and Drive Innovation Culture Partner closely with stakeholders across all major business functions including HR, recruiting, finance, contracts, pricing, delivery, and IT to co-develop AI-enhanced workflows that solve real business problems. Work hand-in-hand with DDC’s Communities of Practice to evangelize agentic thinking, document lessons learned, and promote continuous improvement. Foster a culture of exploration, practical iteration, and ethical experimentation within and beyond the Strategic Innovations Team.
  • Ensure Ethical AI and Compliance in High-Risk Contexts Embed principles of responsible AI, compliance, and Navajo-informed stewardship into all workflows. Implement features that enable transparency, traceability, auditability, and human oversight in every AI deployment. Proactively monitor alignment with security frameworks such as FedRAMP, CMMC, and ITAR, and maintain strict compliance with data classification standards including CUI and PHI. Guide citizen builders in understanding the boundaries of acceptable agent behavior in regulated environments.
  • Test, Optimize, and Maintain AI Workflow Performance Conduct structured testing of all workflows and agentic pipelines across multiple dimensions including latency, retrieval quality, LLM cost usage, error rates, edge cases, and prompt robustness. Identify and resolve performance bottlenecks, model hallucinations, and unreliable interactions with tools or data. Regularly refactor underperforming flows and perform regression testing to ensure platform health as updates or new components are introduced.
  • Demonstrate, Document, and Communicate Measurable Impact Prepare high-quality demonstrations, internal showcases, and pilot rollouts to communicate solution capabilities to DDC leadership, clients, and end users. Maintain complete and accurate documentation of workflows, decision logic, components, testing results, and business impact. Track performance metrics and success indicators such as process efficiency gains, reduced manual effort, faster turnaround time, and improved client satisfaction. Contribute to organizational learning and innovation strategy refinement through formal and informal feedback channels.
  • Must be a U.S. citizen with the ability to obtain and maintain a U.S. government security clearance if required by specific contract or project assignments.
  • Minimum 3 years of professional experience in software development, data engineering, AI engineering, or a similar technical role supporting enterprise-scale systems.
  • At least 1 year of hands-on experience building and shipping generative AI applications or retrieval-augmented generation (RAG) systems that operated in real user-facing environments. Experience must include designing workflows, using modern LLMs, integrating data sources, and solving practical AI delivery challenges.
  • Demonstrated experience owning the lifecycle of AI-driven solutions from concept through deployment. Candidates should be able to provide a portfolio, demonstration artifacts, GitHub repositories, or equivalent examples of real AI systems such as chat assistants, workflow agents, knowledge tools, data extraction pipelines, or proposal-support agents.
  • Experience working with enterprise architectures, ideally including business processes, data architectures, content repositories, and application ecosystems. Familiarity with environments where data is fragmented or inconsistent, and the ability to design AI workflows that operate effectively despite technical debt or process gaps.
  • Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a closely related field.
  • AI Engineering and Workflow Design
  • Strong understanding of large language models, prompt design, context engineering, RAG pipelines, vector stores, semantic search, and multi-step agent orchestration.
  • Ability to design AI workflows that combine LLMs, tooling, structured data, unstructured content, enterprise APIs, and dynamic business rules.
  • Agentic and Pro-Code Development
  • Ability to build advanced agentic logic that supports tool use, multi-step reasoning, fallback behavior, human-in-the-loop decisions, and structured outputs.
  • Skilled in writing custom code within Stack AI compatible environments, including Python and other languages as platform support evolves.
  • Experience creating reusable code nodes, templates, and shared logic components that can be leveraged by both technical and non-technical builders.
  • Platform and Integration Engineering
  • Experience integrating AI workflows with enterprise systems such as SharePoint, Outlook, Teams, Workday, ServiceNow, CRM platforms, or HRIS environments.
  • Ability to design robust connectors, data access patterns, and transformation logic that enable reliable agent interactions with fragmented repositories or inconsistent data structures.
  • Enterprise Architecture Awareness
  • Understanding of enterprise architecture domains including business process architecture, data architecture, application architecture, and infrastructure architecture.
  • Ability to identify architectural gaps that hinder AI workflows and recommend improvements, content restructuring, metadata strategies, or process refinements to strengthen AI readiness across departments.
  • Standardization and Governance
  • Experience developing and maintaining standards, pattern libraries, naming conventions, documentation practices, and guidance for technical teams.
  • Ability to formalize best practices for workflow design, RAG construction, prompt discipline, versioning, scalability, and ethical safeguards.
  • Familiarity with configuration management, environment promotion, version control, testing protocols, and release management for AI workflows.
  • Citizen-Builder Enablement and Mentorship
  • Ability to mentor non-engineering staff who create workflows on no-code and low-code platforms.
  • Experience conducting training, writing internal guides, reviewing citizen-built solutions, and ensuring compliance with security and responsible AI expectations.
  • Problem Solving and Systems Thinking
  • Strong analytical mindset with the ability to reverse-engineer business processes, map data flows, understand upstream and downstream system dependencies, and design AI interventions that meaningfully improve outcomes.
  • Capacity to balance short-term MVP solutions against long-term architectural remediation needs, and to clearly articulate trade-offs to leadership.
  • Platform Stewardship and Vendor Collaboration
  • Ability to serve as a platform champion and represent DDC’s needs to Stack AI’s engineering and product teams.
  • Experience providing structured feedback on features, participating in early access or beta programs, raising quality issues, and influencing platform evolution.
  • Communication and Cross-Functional Collaboration
  • Strong communication skills with the ability to explain AI workflows, limitations, risks, and opportunities to functional leaders, program managers, SMEs, and non-technical stakeholders.
  • Experience working within complex, multi-department environments and building trust across organizational boundaries.
  • Ethical and Responsible AI Competencies
  • Understanding of responsible AI principles, including bias mitigation, fairness, transparency, traceability, and safe deployment practices.
  • Commitment to building AI systems that honor DDC’s heritage values, federal compliance requirements, and the trust placed in the organization by the Navajo Nation and its customers.
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