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About This Role
Job Description Role: AI Solution Architect with strong Experience GenAI \& Agentic AI \- Full Time Role
Location: Onsite – Tampa, Florida
Duration: Fulltime
Experience: 8–15 years of overall IT experience, with at least 3–5 years focused on AI solution architecture and delivery.
Mandatory Skill Tags: AI Solution Architecture, AI solution design, latest AI models, LLMs, enterprise AI architecture, cloud AI/ML platforms, data \& MLOps integration
Secondary Skill Tags: responsible AI, AI governance, vector databases, RAG, semantic search, MLOps tools, cloud\-native architecture, microservices, Kubernetes, agile delivery
Job Summary:
The Onsite AI Solution Architect will lead the end\-to\-end architecture, design, and implementation of AI and AI\-native solutions for Advantive. This role will closely collaborate with business stakeholders, product owners, data teams, and engineering to translate business requirements into scalable, secure, and robust AI architectures. The architect will provide thought leadership on latest AI models and LLMs and ensure best practices, governance, and standards are adopted across AI initiatives.
Key Responsibilities:
- Lead the architecture, design, and technical roadmap for AI and AI\-native solutions aligned to Advantive’s business strategy.
- Translate business and functional requirements into scalable AI solution architectures, covering data, model, application, and integration layers.
- Evaluate, select, and integrate latest AI models and LLMs (including cloud and third\-party services) into enterprise applications and workflows.
- Define reference architectures, patterns, standards, and reusable components for AI solution delivery across the organization.
- Collaborate with data engineers, MLOps engineers, application developers, and product teams to ensure high\-quality, production\-grade AI deployments.
- Establish non\-functional requirements (performance, security, reliability, observability) and ensure AI solutions meet enterprise architecture and compliance guidelines.
- Conduct technical reviews, PoCs, and feasibility assessments for new AI use cases and guide teams on best practices and optimization.
- Provide architectural leadership, mentoring, and guidance to project teams, driving continuous improvement and innovation in AI solution delivery.
Required Skills:
- Strong experience in AI Solution Architecture, designing and delivering enterprise\-grade AI solutions.
- Proven expertise in architectural design involving AI solutions, including end\-to\-end solution blueprints and reference architectures.
- Hands\-on knowledge of designing AI\-based solutions using machine learning, deep learning, and LLM\-based approaches.
- In\-depth understanding of latest AI models and large language models (LLMs), including their capabilities, limitations, and suitable use cases.
- Experience with AI/ML platforms and services (e.g., Azure AI, AWS AI/ML, Google Cloud AI, or equivalent).
- Solid understanding of data architecture concepts, including data pipelines, feature stores, model deployment, and monitoring (MLOps).
- Strong background in application integration patterns (APIs, microservices, event\-driven architecture) for embedding AI into products and workflows.
- Ability to create high\-quality architectural artifacts (HLDs, LLDs, sequence diagrams, data flow diagrams) and communicate them to technical and non\-technical stakeholders.
- Strong stakeholder management, communication, and leadership skills to drive consensus and decision\-making.
Good to Have Skills
- Experience with AI governance, model risk management, and responsible AI practices (fairness, explainability, security, and privacy).
- Familiarity with vector databases, semantic search, RAG (Retrieval\-Augmented Generation), and knowledge\-graph\-based solutions.
- Exposure to MLOps tools and frameworks for CI/CD of ML models and LLM\-based applications.
- Experience in designing multi\-tenant, cloud\-native architectures using containers and orchestration (Docker, Kubernetes).
- Knowledge of enterprise integration with ERP/CRM/line\-of\-business applications.
- Prior experience in leading AI architecture for product\-based or ISV organizations.
- Experience working in agile delivery environments and collaborating with distributed teams.
Educational Qualification
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related discipline from a recognized institution.
Additional Information
All your information will be kept confidential according to EEO guidelines.
Salary Context
This $180K-$200K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Tms Llc, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($190K) sits 13% below the category median. Disclosed range: $180K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Tms Llc AI Hiring
Tms Llc has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Myrtle Point, OR, US, Tampa, FL, US, Campbell, CA, US. Compensation range: $200K - $200K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 Safety roles lead at $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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
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