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About This Role
Company Information
Fríant is a leading manufacturer of innovative, accessible office furniture. Operating at the intersection of style and function, we’re passionate about upholding the ethos that quality craftsmanship and practicality aren’t mutually exclusive. From panel systems and seating to desks, storage, and more, our team is busy year-round creating everything needed for a total office solution. With headquarters and manufacturing in San Leandro, California and a plant in Suffolk, Virginia, our 630,000 square feet of facilities are equipped with state-of-the-art equipment and skilled fabric and woodworkers. We craft product that helps teams get things done while feeling good doing it, regardless of industry — because at Fríant, all work styles are welcome.
Summary/Objective
The AI Engineer designs, develops, deploys, and maintains scalable AI-powered solutions that improve operational efficiency, business intelligence, customer engagement, and process automation across Fríant. This hands-on role combines software engineering, machine learning, large language models, AI automation, and enterprise system integration. The position works with business stakeholders, IT, Customer Success, Operations, Sales, and Product teams to translate complex requirements into secure, production-ready AI solutions and supports the full AI lifecycle from architecture and development through deployment, monitoring, governance, and continuous improvement.
Responsibilities
AI Solution Development
- Design, build, and deploy AI and machine learning solutions that address business challenges and improve operational efficiency.
- Develop and optimize machine learning and deep learning models for predictive analytics, classification, recommendation systems, and automation.
- Build applications using large language models, Generative AI, and intelligent agents.
- Design Retrieval-Augmented Generation architectures that securely connect enterprise knowledge bases and business systems.
AI Automation and Agents
- Develop and manage AI agents that automate workflows, support business operations, and improve employee productivity.
- Create intelligent document processing solutions for extracting, classifying, and analyzing structured and unstructured data.
- Implement conversational AI solutions, virtual assistants, copilots, and chatbot platforms.
- Design workflow automation using AI orchestration platforms, APIs, function calling, and tool-use architectures.
Data and Integration
- Build and maintain reliable data pipelines for model training, inference, evaluation, and analytics.
- Integrate AI solutions with CRM, ERP, customer support, eCommerce, knowledgebase, and other enterprise applications.
- Develop APIs, microservices, webhooks, middleware, and event-driven architectures that support AI deployment.
- Partner with data engineers, IT, and business teams to define practical and scalable AI architecture.
AI Operations and Governance
- Monitor model and application performance, reliability, quality, cost, and business impact.
- Implement MLOps and LLMOps practices for deployment, version control, automated evaluation, monitoring, and lifecycle management.
- Apply responsible AI, privacy, security, data governance, and access-control requirements.
- Establish guardrails and risk controls for AI applications, and continuously improve accuracy, latency, scalability, and efficiency.
- Create and maintain technical documentation, architecture diagrams, operating procedures, and user guidance.
Competencies
- Strong analytical, critical-thinking, troubleshooting, and problem-solving abilities.
- Ability to translate business challenges and user requirements into practical AI solutions.
- Excellent written and verbal communication, stakeholder collaboration, and technical documentation skills.
- Ability to work independently while contributing effectively across cross-functional teams.
- Comfort working in a fast-paced environment where technologies, priorities, and requirements evolve.
- Strong commitment to responsible AI, security, privacy, and data governance.
Required Education and Experience
- Bachelor’s degree in computer science, Artificial Intelligence, Data Science, Software Engineering, or a related field; Master’s degree preferred.
- 3+ years of hands-on experience developing and deploying AI or machine learning solutions in production environments.
- Strong proficiency in Python and practical experience with SQL, Git/GitHub, REST APIs, and at least one application framework such as FastAPI or Flask.
- Experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, or Hugging Face Transformers.
- Strong understanding of supervised and unsupervised learning, deep learning, neural networks, natural language processing, feature engineering, and model evaluation.
- Hands-on experience with enterprise Generative AI and LLM technologies such as OpenAI APIs, Azure OpenAI, Anthropic APIs, LangChain, LlamaIndex, Semantic Kernel, MCP, prompt engineering, fine-tuning, function calling, AI agents, and RAG.
- Experience with vector databases or search platforms such as Pinecone, Weaviate, FAISS, Chroma, or Azure AI Search.
- Experience with at least one major cloud platform, including Microsoft Azure, AWS, or Google Cloud Platform.
- Knowledge of Docker, Kubernetes, CI/CD pipelines, Infrastructure as Code, MLflow, model versioning, observability, performance monitoring, or automated evaluation frameworks.
Preferred Qualifications
- Experience implementing AI within CRM, ERP, customer service, eCommerce, or knowledge-management platforms.
- Experience with Zoho CRM, Salesforce, HubSpot, Microsoft Dynamics, Microsoft 365, Teams, SharePoint, or Power Platform.
- Knowledge of OCR, document intelligence, intelligent document processing, analytics, reporting, or forecasting solutions.
- Experience supporting Customer Success, Operations, Sales, manufacturing, or warehouse-distribution environments.
- Experience with JavaScript or TypeScript and AI observability or prompt-monitoring platforms.
- Relevant Azure AI, AWS AI/ML, Google Cloud, or other industry certifications are a plus.
Work Environment
While performing the duties of this job, the employee operates in both a professional office environment and production environment, when necessary. Appropriate personal protective equipment will be required when visiting production areas.
Physical Demands
The physical demands described here are representative of those required to perform the essential functions of the position. The employee is regularly required to communicate, use a computer and related equipment, and remain seated or stand for extended periods.
Position Type/Expected Hours of Work
This is a full-time position working on a regular Monday-Friday schedule, with occasionally early morning, evening or weekend duties. All employees receive support from San Leandro. As there is a second plant located in Suffolk, VA, and as there are employees working remotely from other various U.S. cities, the occasional need may arise for working outside of San Leandro hours.
Travel Requirements
Travel to our Suffolk, VA plant/office may be required in rare situations and for periods of a week or less
Supervisory Responsibility
This position has no supervisory responsibilities
**Disclaimer: This job description is not intended to be an exhaustive list of all duties, responsibilities, or qualifications associated with the job. While this is intended to be an accurate reflection of the current job, management reserves the right to revise the job or to require that other or different tasks be performed as assigned.
Job Type: Full-time
Pay: $135,000.00 per year
Benefits:
- 401(k)
- Dental insurance
- Employee discount
- Flexible spending account
- Health insurance
- Paid time off
- Vision insurance
Application Question(s):
- Will you now or in the future require visa sponsorship?
- Are you located in the Bay Area/California?
Education:
- Bachelor's (Required)
Experience:
- AI/ML development: 3 years (Required)
Ability to Commute:
- San Leandro, CA 94577 (Required)
Work Location: In person
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Friant & Associates, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Friant & Associates AI Hiring
Friant & Associates has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Leandro, CA, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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