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About This Role
JOB OVERVIEW
Plan A Technologies is looking for a Solutions Architect with AI\-Assisted Development Focus. We are building an agentic AI development platform that embeds AI deeply into software delivery workflows — from design and coding to testing, review, and operations.
We are looking for a Solutions Architect with strong coding skills who can design, build, and evolve production\-grade agentic AI systems. This role sits at the intersection of software architecture, AI\-assisted development, and platform engineering.
This is not a research or data\-science role. The focus is on real\-world systems, developer productivity, and operational excellence.
Please note: you must have at least 10\+ years of experience as Solutions Architect to be considered for this role.
JOB RESPONSIBILITY
AI Platform \& Provider Integration
- Design and integrate AI solutions using major cloud AI providers such as OpenAI, Anthropic (Claude) Google (Gemini).
- Architect provider\-agnostic AI layers, including:
- Model abstraction and routing
- Fallback strategies
- Multi\-model orchestration
- Evaluate new models and providers with a pragmatic, cost\-aware mindset.
Agentic AI Architecture \& Patterns
Design and implement agentic AI systems, including:
- Single\-agent and multi\-agent architectures
- Planner / executor / reviewer loops
- Tool\-calling and function\-calling patterns
- Event\-driven and workflow\-based agent orchestration
- Define interaction patterns between AI agents, backend services, APIs, and human users.
- Balance autonomy with control using guardrails and human\-in\-the\-loop mechanisms.
AI\-Assisted Development Enablement
Actively use AI to accelerate software development, including:
- Code generation and refactoring
- Test creation and validation
- Documentation and architecture assistance
- Define standards, patterns, and best practices for developers working with AI\-assisted tools.
- Partner with engineering teams to embed AI into daily development workflows.
Software Architecture \& Hands\-On Coding
- Design scalable, secure, and observable systems that embed AI as a first\-class architectural component.
- Write and review production code (APIs, services, integrations) to validate architectural decisions.
- Make pragmatic trade\-offs between flexibility, maintainability, cost, and delivery speed.
Cost, Performance \& Reliability
- Monitor and optimize AI system usage, including:
- Token and cost management
- Latency and throughput
- Model performance vs. cost trade\-offs
- Design architectural controls to prevent cost explosion in agentic systems.
- Implement strategies for graceful degradation and safe failure modes.
AI Operations, Governance \& Safety
Design guardrails against:
- Prompt injection
- Data leakage
- Unintended agent behavior
- Implement logging, tracing, and evaluation pipelines for AI\-driven workflows.
- Support safe rollout strategies, experimentation, and continuous improvement of AI systems.
EXPERIENCE
- BS in Computer Science, Software Engineering or equivalent practical experience
- Strong background in software architecture and distributed systems
- Hands\-on coding experience (language\-agnostic; backend experience required)
- Practical experience integrating and operating LLM\-based systems in production
- Deep understanding of agentic AI concepts and patterns
- Experience designing systems used by developers (internal platforms, APIs, tooling)
- Strong communication skills to collaborate with engineering, product, and leadership teams
- Excellent organizational, problem solver, and analytical abilities
- Excellent verbal and written English communication skills
- Initiative and drive to do great things
Nice to haves:
- Experience with Retrieval\-Augmented Generation (RAG) and vector databases
- Familiarity with LLM evaluation frameworks and quality metrics
- Experience building internal developer platforms or AI enablement initiatives
- Understanding of fine\-tuning trade\-offs and when model tuning is appropriate (not required)
- Exposure to platform observability tools for AI systems
ABOUT THE COMPANY/BENEFITS
Plan A Technologies is an American software development and technology advisory firm that brings top\-tier engineering talent to clients around the world. Our software engineers tackle custom product development projects, staff augmentation, major integrations and upgrades, and much more. The team is far more hands\-on than the giant outsourcing shops, but still big enough to handle major enterprise clients.
Location: Work From Home 100% of the time, or come in to one of our global offices. Up to you.
Great colleagues and an upbeat work environment: You'll join an excellent team of supportive engineers and project managers who work hard but don't ever compete with each other.
Benefits: Vacation, Brand New Laptop, and More: You’ll get a generous vacation schedule, and other goodies.
*If this sounds like you, we'd love to hear from you!*
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 Plan A Technologies, 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.
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.
Plan A Technologies AI Hiring
Plan A Technologies has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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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