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About This Role
Our company is building an automation system for procurement and sales of aircraft components. The system automates the full RFQ cycle: receiving requests from the ERP, selecting suppliers, sending requests, processing replies with AI, generating quotes, and preparing commercial proposals for customers.
The solution is already operating on real RFQs and commercial data. The next step is moving from automating individual operations to a governed decision\-making system that can independently handle a significant share of standard transactions and route only complex, high risk, and non standard cases to humans.
The strategic goal is to evolve Banner Copilot into an AI\-supported operating and decision\-making layer for Banner Aircraft International.
The platform should gradually automate standard account\-management and operational workflows, support management decisions with data\-driven recommendations, identify business and financial risks, and enable a leaner operating model over time.
The platform should first provide decision support and next\-best\-action recommendations, and progressively take autonomous actions in clearly defined, low\-risk scenarios. Over time, it should help prioritize opportunities, support resource allocation, identify financing needs, assess risks around customers, suppliers, margin and cash flow, and recommend concrete management actions.
The role
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We are looking for a hands\-on AI Business Automation Architect who can help develop Banner Copilot not only as a procurement and sales automation platform, but as an AI\-supported operating and decision\-making system for the company.
The architect will work with the existing product and codebase, understand Banner’s operational and commercial processes, identify which activities and decisions can be automated, and help the engineering team bring practical solutions to production.
This is not a purely advisory role. The architect must understand the existing codebase, participate in prototyping critical components, run architectural reviews, and work with the engineering team to bring solutions to production.
The primary outcome of the role is not architecture documentation itself, but a working system that reduces manual account\-management and operational work, increases transaction\-processing speed, improves decision quality, identifies business and financial risks, and provides management with concrete, data\-supported recommendations.
Responsibilities
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- Product architecture
- Audit the current architecture
- Identify which account\-management and operational functions can be fully or partially automated
- Map the key operational and management decisions currently made by employees
- Define the transition from decision support to controlled automation and limited autonomy
Define the target architecture for the full cycle: RFQ Quote Sales Order Delivery Close Follow\-up* Management Recommendation
- Set clear boundaries between business rules, algorithms, statistical models, and AI agents
- Prepare a phased technical roadmap for evolving from the current solution to an autonomous engine
- Design a scalable, maintainable architecture to replace the current monolithic setup
- Define requirements for data, logging, observability, fault tolerance, and security
- AI and decision automation
- Design AI‑agent workflows for supplier requests, web search, and retrieval of up‑to‑date prices
- Define architecture for using LLMs, tools, confidence scoring, and human‑in‑the‑loop
- Develop exception‑handling rules, including AOG cases, high deal value, and low model confidence
- Determine where AI is truly needed versus where formal rules are more reliable
- Contribute to the development and testing of prompt logic, extraction pipelines, and agent orchestration
- Design transaction\-prioritization logic based on margin, probability of closing, timing, customer risk, supplier risk and cash impact
- Define which decisions Copilot may make independently and which require human or C\-level approval
- Design next\-best\-action recommendations for account managers and operational teams
- Establish auditability, explainability and approval controls for AI\-supported decisions
- Pricing and decision‑making
- Design a Pricing Engine
- Define the architecture for calculating margin, deal probability, supplier risk, and commercial attractiveness
- Separate rule‑based, statistical, and ML approaches
- Plan the use of historical data from eMARS and other price sources
- Define a model for selecting the best price factoring recency, supplier reliability, lead times, and risk
- Management intelligence
- Design a management\-recommendation layer that explains what is happening, where the risks are, what options are available, and what action is recommended
- Define regular AI\-generated management reports covering sales pipeline, margin opportunities, cash requirements, customer risk, supplier risk and operational bottlenecks
- Design business\-analysis and SWOT capabilities for the company, individual business lines and key customers
- Support budget\-allocation and financing decisions with data\-driven recommendations
- Ensure that Banner Copilot does not only display information, but proposes concrete next actions
- Engineering execution
- Collaborate with the lead engineer and developers
- Participate in implementing critical components and prototypes
- Conduct architecture and code reviews
- Establish technical standards and engineering practices
- Help decompose the architecture into deliverable phases and tasks
- Ensure implementation conforms to the approved architecture
- Current system modules
- ERP ingestion: MySQL adapter, RFQ input, daemon and manager
- Vendor selection: DuckDB matching, ranking, blacklist, database of 9,000\+ suppliers
- Messaging: Gmail service account, email generation, dispatcher, deduplication, and cooldown
- Reply processing: Gmail auditor, PDF/XLSX/OCR parsing, Claude‑based extraction, confidence scoring
- Storage: SQLite WAL, sent registry, async audit logger
- API and interfaces: FastAPI, Tabulator.js, Sent Emails Dashboard, RFQ Workbench
Requirements
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- 5\+ years of backend development and architecture experience
- Strong Python: async, FastAPI, SQLAlchemy or similar tools
- Experience designing systems with LLM APIs, AI agents, and tool use
- Understanding of event‑driven architecture, queues, and asynchronous pipelines
- Experience designing integrations with ERP, CRM, or other enterprise systems
- Experience evolving an MVP or monolith into a scalable architecture
- Ability to judge where to apply AI versus conventional business logic
- Experience with production systems and real business transactions
- Ability to translate business problems into architecture and a coherent technical roadmap
- Experience designing decision engines, recommendation systems or scoring logic.
- Ability to work with incomplete, inconsistent and operational business data.
- Ability to communicate with business users, account managers and senior management.
- Ability to translate business goals into operating workflows, system architecture and phased implementation.
- Understanding of financial and operational decision factors such as margin, cash flow, financing needs and resource constraints.
- Working proficiency in English
Nice to have
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- Experience in procurement, B2B sales automation, or supply chain
- Knowledge of aviation MRO and the aircraft components market
- Experience with the Claude API
- Prompt engineering and confidence‑scoring experience
- Knowledge of ILS, PartsBase, or similar aviation platforms
- Experience building human‑in‑the‑loop processes
- Experience developing pricing, recommendation, or decision engines
- Experience with web scraping and real‑time price retrieval
- Experience with account\-management automation
- Experience with cash\-flow forecasting, budget\-allocation tools or financial decision support
- Experience building systems that generate management recommendations
- Experience with operational analytics and business\-process redesign
What we offer
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- Involvement in a strategically important AI product
- Opportunity to influence system architecture and long\-term development
- Flexible schedule and remote work
- A culture that encourages experimentation, R\&D, and implementation of new approaches
- Room for professional growth
Apply by filling in the contact form or sending your CV to [email protected]
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 XIM, Inc., 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.
XIM, Inc. AI Hiring
XIM, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,334 across 717 positions. About 14% of all AI roles offer remote work.
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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