Interested in this AI/ML Engineer role at A+ Federal Credit Union?
Apply Now →Skills & Technologies
About This Role
Marketing Campaign \& Project Manager
Job Type: Full\-Time
Exemption Type: Exempt
Wage Amount: $75,000 yearly minimum
General Summary – Primary Functions
---------------------------------------
The primary purpose of this position is to lead the planning, execution, and optimization of integrated marketing campaigns that drive member engagement, product adoption, brand awareness, and help the credit union achieve established goals.
This role will serve as a strategic partner across departments and with external agencies, ensuring marketing initiatives are aligned with business goals and delivered on time and within budget. Collaborate with key stakeholders across A\+FCU to plan and execute cross\-functional projects. Provide outstanding internal service.
2\. Campaign Strategy \& Execution
--------------------------------------
- Collaborate with Marketing leadership to develop and manage multi\-channel marketing campaigns (digital, print, email, social, in\-branch) to support promotions, member communications, and product launches.
- Team up with internal stakeholders to define campaign objectives, target audiences, messaging, and KPIs.
- Manage campaign timelines, budgets, and performance metrics.
- Communicate across the credit union on Marketing promotions, special events, and campaigns.
- Build out marketing deliverable plans using project management expertise and technology solutions to facilitate the team’s ability to achieve deadlines and support A\+FCU priorities.
- Work with Compliance teams to ensure marketing materials have proper disclosures and targeting is non\-discriminatory.
- Work with marketing team members to ensure consistency across campaign materials, channels, and vendors.
- Coordinate with external vendors and media partners as needed.
3\. Data \& Performance Analysis
------------------------------------
- Monitor campaign performance through collaboration with Enterprise Analytics, Digital Marketing, and using tools such as Google Analytics, Google Ads, Marketo, Marketing dashboards, etc. and provide actionable insights for optimization.
- Generate reports and insights to optimize future campaigns and improve ROI.
4\. Marketing Project Management
------------------------------------
- Lead project teams to deliver marketing initiatives from concept to completion.
- Create and manage detailed project plans, timelines, and budgets.
- Coordinate with vendors, agencies, and internal teams to ensure timely delivery of marketing assets and approvals.
- Identify and mitigate risks, resolve issues, and ensure stakeholder alignment throughout the project lifecycle.
- Participate in organization projects as a representative of the Marketing team.
5\. Collaboration \& Communication
--------------------------------------
- Serve as a liaison between marketing, product, digital, and member services teams to ensure seamless campaign execution.
- Facilitate regular project updates, meetings, and status reports to marketing leadership.
- Conduct monthly update meeting with supervisor.
- Champion a culture of continuous improvement and innovation.
- All other duties as assigned.
Education and Experience
----------------------------
- Bachelor’s degree in Marketing, Communications, Business, or related field required
- Three years of experience in marketing campaign management required (financial services or a regulated industry highly preferred).
- Two years of experience in project management preferred.
Knowledge, Skills, \& Abilities
-----------------------------------
- Strong understanding of digital marketing channels and campaign performance metrics.
- Strong project management skills; able to manage deadlines and multiple projects at varying stages of completion.
- Proficiency in marketing automation platforms (e.g., Marketo), project management tools (e.g., Wrike, Trello, Monday.com), and other Marketing platforms (e.g., WordPress, Google Ads, Google Analytics, etc.).
- Proficiency in Microsoft products (Word, Excel, PowerPoint, Power BI, etc.).
- Adept at problem solving.
- Experience with campaign development.
- Professional verbal, written and presentation communication skills. Ability to successfully tailor communications according to the audience, and the ability to interact with diverse groups of people both internally and externally in a professional and diplomatic manner to achieve a common goal.
- Analytical mindset with the ability to interpret data and make data\-driven decisions.
- Capacity to work independently and as a team member while using discretion in decision making and sound judgment in problem solving. Think creatively and innovate.
- Excellent writing, editing, and proofreading skills; strong attention to detail.
- Skill in handling multiple tasks at one time and able to prioritize accordingly.
- Requires a high degree of professionalism and the ability to handle confidential information.
- Understanding of financial institutions, products and services, and financial education.
Desirable Traits
--------------------
- Utilizes stress in a productive manner and works well under pressure.
- Ability to lead, inspire and build trust.
- Fosters strong computing ethics and integrity
- Able to work flexible working hours and in emergency situations
- Dependable
- Future Thinker
- Negotiation Skills
Cognitive \& Physical Functions
-----------------------------------
- Must have the ability/stamina to work at least 40 hours a week
- Will frequently reach, feel, bend, stoop, carry, finely manipulate and key in data
- Must be able to communicate heavily through telephone, email, and in\-person communications
- Must be able to engage in problem\-solving skills to help identify and solve potential issues in the field
Decision\-Making Authority
------------------------------
Types of Decisions made independently:
Approved marketing budget items and development and types of promotions.
Types of Decisions requiring supervisory approval:
Purchases in accordance with CU policy and travel approval. Any situations outside A\+FCU policies and procedures.
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At A+ Federal Credit Union, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
A+ Federal Credit Union AI Hiring
A+ Federal Credit Union has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.