Interested in this AI/ML Engineer role at Dynatech International?
Apply Now →Skills & Technologies
About This Role
Job Title: Senior AI System Architect (Outcome\-Based AI Operations)
Department: Information Technology
Reports to: Vice President of Information Technology
Location: Palm Bay, FL
Company Overview
Company Overview: Established in 1973, Dynatech International is a Commercial and Defense supply chain company providing long term, complex procurement, engine overhaul, rotable and repair management services, manufacturing, and kitting solutions across land, air, sea, and space programs. Dynatech’s proprietary database, the Defense Logistics Management System (DLMS®), empowers us to mitigate supply chain risk, and provide quality supply chain solutions in a cost\-effective manner that enhances operational readiness for over 2,000 weapons systems and platforms.
We're actively deploying AI\-driven operational systems to modernize sourcing, procurement, proposal development, workflow automation, executive operations, and enterprise decision support.
This is not a traditional "AI chatbot" position.
We're building production\-grade AI operational infrastructure designed to create measurable business outcomes, improve operational velocity, enhance executive decision\-making, and support scalable autonomous workflows across the enterprise.
We're looking for an AI Systems Architect to help design, implement, and mature the next generation of AI\-enabled operational systems at Dynatech.
Position Summary
The Senior AI Systems Architect will lead the design and implementation of enterprise\-grade AI operational systems integrating:
- large language models (LLMs) · autonomous agents
- workflow orchestration · retrieval systems
- Microsoft ecosystems · operational data sources
- human\-in\-the\-loop governance
This role takes more than API integration and prompt engineering. The architect needs to design durable AI systems that produce measurable operational outcomes while maintaining security, auditability, governance, scalability, and operational resilience.
The right candidate is equally at home discussing:
- AI orchestration frameworks · workflow engineering
- executive operations · systems integration
- long\-term production architecture
Core Responsibilities
AI Systems Architecture
- Architect scalable AI operational systems supporting enterprise workflows
- Design modular, maintainable AI infrastructure
- Build autonomous and semi\-autonomous AI agent ecosystems
- Develop long\-term memory and retrieval architectures
- Implement human\-in\-the\-loop escalation frameworks
- Design secure production\-grade AI pipelines
Workflow Automation
- Automate operational and executive workflows
- Integrate AI into procurement, sourcing, proposal generation, and operational processes
- Develop measurable KPI\-driven automation systems
- Create AI\-assisted decision support systems
- Design orchestration layers across multiple AI services and tools
Microsoft Ecosystem Integration
- Integrate with Microsoft 365, Azure AI, Microsoft Graph, Teams, Outlook, and SharePoint
- Support AI\-driven email, scheduling, task management, and operational workflows
- Implement secure permission and identity management practices
AI Engineering and Orchestration
- Implement multi\-agent orchestration frameworks
- Develop RAG (Retrieval\-Augmented Generation) systems
- Build vector database architectures
- Implement tool\-use and function\-calling frameworks
- Develop monitoring, logging, and audit systems
- Design AI safety and governance controls
Operational Governance
- Develop production\-grade testing methodologies
- Implement AI guardrails and escalation logic
- Ensure operational transparency and auditability
- Support data governance and compliance requirements
- Build systems resilient to hallucination and workflow failure
Required Qualifications
Compliance and Eligibility (non\-negotiable)
- U.S. Person as defined in 22 CFR § 120\.62 (U.S. citizen, lawful permanent resident, or protected individual)
- Familiarity with CMMC Level 2, NIST SP 800\-171, and DFARS 252\.204\-7012 requirements, and with handling Controlled Unclassified Information (CUI)
- Comfortable operating in an ITAR\-aware environment
- Production system access will not be granted until our standard vendor risk review has been completed
Technical Expertise
- Deep experience with OpenAI APIs and LLM architectures
- Strong understanding of AI orchestration frameworks
- Experience with one or more of:
- Microsoft Foundry
- LangChain
- LangGraph
- CrewAI
- AutoGen
- Semantic Kernel
- Microsoft Copilot Studio
- or similar frameworks
- Experience with vector databases and retrieval systems
- Strong API integration experience
- Strong Python development capabilities
- Experience with Azure AI and Microsoft Graph
- Experience with workflow orchestration platforms
Legacy System Integration
- Experience integrating modern AI services with legacy enterprise applications
- Strong SQL Server background (T\-SQL, performance diagnostics, schema discovery)
- Comfort with ASP.NET Web Forms and ERP\-class applications as integration targets
- Comfort working across Windows Server, macOS AI compute, and Tailscale\-managed networks
Systems and Operations
- Track record designing durable operational systems
- Experience implementing production AI systems
- Strong understanding of workflow engineering
- Experience with logging, monitoring, and observability
- Strong understanding of security and governance
- Experience with scalable enterprise architecture
Business and Strategic Thinking
- Ability to align AI systems with measurable business outcomes
- Strong operational reasoning skills
- Ability to translate executive objectives into technical architectures
- Strong written and verbal communication skills
- Ability to operate independently in a fast\-moving environment
Preferred Qualifications
- Experience supporting defense or regulated industries
- Experience with procurement or supply chain systems
- Experience with autonomous operational workflows
- Experience with voice AI systems
- Experience with avatar or conversational AI systems
- Experience integrating AI into ERP or CRM environments
- Experience with executive assistant AI systems
What Success Looks Like
Success in this role means building AI systems that:
- Reduce operational friction
- Improve decision speed
- Enhance sourcing and proposal operations
- Automate repetitive workflows
- Improve executive leverage
- Maintain governance and security
- Scale reliably across the organization
What This Role Is NOT
This role is NOT:
- Basic prompt engineering
- Simple chatbot development
- Front\-end\-only AI work
- Experimental hobby AI projects
We're looking for a systems architect capable of building operational AI infrastructure with real\-world business impact.
Ideal Candidate Traits
The ideal candidate:
- Thinks in systems
- Understands operational workflows
- Values measurable outcomes
- Embraces rapid innovation
- Balances speed with governance
- Operates at both strategic and technical level
Application Requirements
Applicants should provide:
- Resume or CV
- Relevant GitHub or portfolio links
- Examples of production AI systems built
- Architecture examples or diagrams (if available)
- Description of prior AI operational workflows implemented
- Preferred AI stack and orchestration frameworks
- Availability and engagement model
Compensation is competitive and aligned to experience, capability, and demonstrated expertise. Both contract and long\-term engagement structures will be considered
We offer a comprehensive benefits package which includes health, dental, vision, \& life insurance, and 401k Retirement plan.
Equal Opportunity Employer \- Vet/Disability \- Drug\-Free Workplace
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Dynatech International, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Dynatech International AI Hiring
Dynatech International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Palm Bay, FL, US.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.