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About the Role
Ianai Systems is looking for an Implementation Project Manager to lead customer implementations of ianaiERP, our AI-native ERP platform for manufacturing companies.
This is different from a traditional ERP consultant role.
Our AI-assisted implementation system handles much of the system configuration and technical work. You do not need coding experience or deep expertise in a specific ERP system.
We are looking for someone who understands how businesses operate, particularly manufacturing and supply chain, and can work closely with customers to understand their workflows, manage implementation projects, and coordinate with our product and development teams.
You will work closely with the CEO and play an important role in building our U.S. implementation organization.
What You Will Do
- Lead ERP implementations from kickoff through go-live
- Conduct regular meetings with customers throughout implementation
- Understand and document customer business processes and workflows
- Learn how customers manage sales, purchasing, inventory, manufacturing, warehouse, accounting, and related operations
- Translate customer needs into clear requirements for our product and development teams
- Coordinate implementation schedules, priorities, testing, data migration, training, and go-live
- Educate customers on the system and new workflows
- Identify process gaps and implementation risks
- Manage approximately 2–3 customer implementations simultaneously
- Coordinate closely with our U.S. team and India-based development team
What We Are Looking For
- 3+ years of experience in project management, software implementation, business operations, manufacturing, supply chain, or a related field
- Good understanding of manufacturing and/or supply-chain processes
- Strong ability to understand how different businesses operate
- Excellent customer communication and meeting-management skills
- Comfortable communicating with executives, managers, operational users, and software developers
- Strong organization and follow-through
- Ability to manage multiple projects and priorities
- Comfortable working in a fast-moving startup environment
- Strong problem-solving skills and curiosity about business processes
ERP Experience
Deep ERP expertise is not required.
Experience with ERP, MRP, manufacturing software, inventory systems, accounting software, or other business software is preferred, but we value business-process knowledge and customer-facing ability more than expertise in any particular ERP platform.
No programming experience is required.
Preferred Experience
Experience in any of the following is a plus:
- Manufacturing
- Job shops or custom manufacturing
- Distribution
- Supply chain
- Inventory and warehouse operations
- Purchasing and procurement
- Production planning
- Business process analysis
- ERP or business software implementation
- Working with India-based or offshore software development teams
Who Will Do Well in This Role
You may be a strong fit if you enjoy talking with customers, learning how their businesses actually work, solving operational problems, and making sure projects get completed successfully.
We are not looking for someone who only manages schedules and sends status reports. We want someone who can understand the customer's operation, identify what needs to happen, communicate clearly with both customers and developers, and drive the implementation forward.
Compensation
$70,000–$90,000 per year, depending on experience.
Benefits and performance-based compensation may be available.
Occasional travel to customer locations may be required.
Pay: $70,000.00 - $90,000.00 per year
Benefits:
- 401(k) matching
- Paid time off
Application Question(s):
- Have you worked with ERP, MRP, inventory, manufacturing, accounting, or other business management software?
- How many projects have you implemented?
- How many implementation or customer projects have you managed simultaneously?
Experience:
- project management or software implementation: 3 years (Required)
- manufacturing, distribution and inventory: 2 years (Required)
Work Location: Hybrid remote in Anaheim, CA 92807
Salary Context
This $70K-$90K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Ianai Systems 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($80K) sits 62% below the category median. Disclosed range: $70K to $90K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Ianai Systems Inc AI Hiring
Ianai Systems Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Anaheim, CA, US. Compensation range: $90K - $90K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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