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About This Role
FoxFarm Soil \& Fertilizer Company is are seeking an exceptionally skilled Data, AI \& Network Integration Engineer for a critical hybrid role at our Samoa, CA location.
This non\-exempt (hourly) position is responsible for the full lifecycle of our data—from ensuring the performance and security of on\-premise SQL Server environments to engineering high\-speed data pipelines into Azure and integrating that data with Azure AI and Machine Learning services. A key component of this role is managing the Cisco\-based network infrastructure that ensures reliable, low\-latency connectivity between our on\-premise data centers and the Azure cloud. The ideal candidate has a background in database management or full\-stack development with broad skills in cloud integration, network automation, and applied AI.
Key Responsibilities:
- *Network \& Connectivity:* Configure and maintain network connectivity (e.g., Azure ExpressRoute, VPNs, Cisco routers/switches) to ensure seamless data flow between on\-premise systems and cloud services.
- *AI Integration:* Design, build, and maintain integrations with Azure AI Services (e.g., Azure OpenAI, Cognitive Search) and manage the underlying network security protocols.
- *Management \& Performance:* Install, configure, maintain, and tune MS SQL Server databases; monitor network bandwidth and latency to optimize database synchronization and AI inference speeds.
- *Infrastructure Automation:* Utilize Cisco DevNet principles, PowerShell, and Python to automate network provisioning, data preparation, and AI service monitoring.
- *Security \& Compliance:* Manage database security, network Access Control Lists (ACLs), and data masking; implement AI\-driven threat detection to monitor for anomalies across both data and network layers.
- *Prompt Engineering:* Develop, test, and refine prompts for generative AI and Large Language Models (LLMs) to ensure accurate and consistent outputs for business applications.
- *ML Data Preparation:* Collaborate with data scientists to cleanse and structure datasets for ML model training, optimizing data placement across the network to minimize latency.
- *Testing \& Quality:* Write unit/integration tests and perform network failover testing to ensure total system resilience and high availability.
Integration \& Business Analysis
- *Cloud Integration:* Set up, manage, and monitor data pipelines between on\-premise systems and Azure, managing the underlying Cisco network fabric.
- *Development Support:* Support development teams by reviewing database and network interaction code (primarily .NET C\#, with exposure to Python).
- *Documentation:* Create and maintain detailed documentation of data flows, network architectures, and AI integrations.
*Required Qualifications*
- 3\-5\+ years of experience as a Microsoft SQL Server DBA and/or Strong proficiency in C\# and the .NET ecosystem.
- Cisco Networking: Proven experience with Cisco routing, switching, and hybrid cloud connectivity.
- Azure AI Expertise: Hands\-on experience integrating with Azure AI Services and OpenAI.
- Database Mastery: Strong proficiency in writing and optimizing complex T\-SQL and stored procedures.
- AI Techniques: Demonstrable experience with prompt engineering and data preparation for ML models.
- Automation: Solid experience using PowerShell or Python for infrastructure and database automation.
*Preferred Qualifications:*
- Certifications: Cisco CCNA/DevNet or Microsoft Azure DP\-300/AI\-102\.
- High Availability: Experience with SQL Server Always On Availability Groups and Cisco hardware redundancy.
- Data Tools: Experience with Azure Data Factory or SSIS for hybrid data integration.
- Education: Bachelor’s degree in Computer Science, Information Technology, or equivalent experience.
Pay: $80,000\.00 \- $120,000\.00 per year
Benefits:
- 401(k)
- 401(k) matching
- Dental insurance
- Employee assistance program
- Employee discount
- Health insurance
- Life insurance
- Paid time off
- Retirement plan
- Vision insurance
People with a criminal record are encouraged to apply
Work Location: In person
Salary Context
This $80K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At FoxFarm Soil & Fertilizer Company, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($100K) sits 53% below the category median. Disclosed range: $80K to $120K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
FoxFarm Soil & Fertilizer Company AI Hiring
FoxFarm Soil & Fertilizer Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Samoa, CA, US. Compensation range: $120K - $120K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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