Diné Development Corporation · 1 hour ago
Agentic AI Platform Engineer
United States
Full-time
Remote
Mid Level
3+ years expDiné Development Corporation (DDC) is a Navajo Nation owned family of companies that delivers IT, professional, and environmental solutions to advance the missions of federal, state, and tribal government agencies. The AI Engineer will be responsible for designing, developing, and deploying advanced AI solutions on DDC’s Stack AI platform, focusing on building autonomous AI workflows powered by large language models to enhance operational efficiency and client value.
Executive Office
No H1B
Security Clearance RequiredU.S. Citizen Only
Responsibilities
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Qualification
Required
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
Preferred
Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a closely related field
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
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
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
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
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
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
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
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
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
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
Company
Diné Development Corporation
Diné Development Corporation (DDC) is a family of companies that delivers IT, engineering, and professional services solutions that solve the dynamic challenges of federal agencies.
501-1000 employees
Funding
Current Stage
Late StageLeadership Team
Austin Tsosie, MBA
Chief Executive Officer
Jacqueline Murray
Chief Operating Officer
Company data provided by crunchbase