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About This Role
Position Overview:
The AI Solutions Architect is a leadership role integrating strategic technology planning, solution architecture, and enterprise architecture across all areas of the business. This role is responsible for creating the overall technical vision and architecture for enterprise AI solutions that address business needs and support the organization’s broader technology strategy.
The AI Solutions Architect designs, describes, and governs the enterprise architecture that enables AI products developed and delivered by the AI engineering and product teams. The role focuses on API integration design, data architecture, large language model deployment patterns, conversational AI platform integration, security, scalability, reliability, and observability.
Working closely with the AI team, Enterprise Architecture, Infrastructure Architecture, Data, Cybersecurity, product leaders, and business stakeholders, this position ensures that AI solutions integrate effectively with the enterprise technology ecosystem and align with established architectural standards, data strategies, security policies, and technology roadmaps.
Responsibilities:
- Create the overall technical vision and architecture for enterprise AI solutions that address business needs.
- Create and maintain documentation of the AI technology ecosystem, architecture, integrations, data flows, and platform dependencies.
- Define scalable and secure architecture patterns for generative AI, large language models, machine learning, conversational AI, and related enterprise services.
- Design API and integration architectures connecting AI products with enterprise applications, data platforms, third\-party services, and legacy systems.
- Define data architecture requirements for AI solutions, including data access, movement, quality, lineage, privacy, and governance.
- Establish architectural patterns for large language model deployment, model services, retrieval\-augmented generation, grounding, inference, and prompt management.
- Provide architectural guidance for integrating conversational AI platforms with digital channels, voice platforms, contact center technologies, enterprise systems, and customer data.
- Collaborate with AI product and engineering teams, Enterprise Architecture, Infrastructure, Data, and Cybersecurity to translate business and product requirements into scalable technical solutions.
- Provide architecture oversight for multiple concurrent AI initiatives and ensure alignment with enterprise technology standards, security policies, and strategic roadmaps.
- Evaluate and recommend AI platforms, model services, integration technologies, data services, and reusable enterprise capabilities.
- Research emerging AI technologies and recommend long\-range architecture strategies and standards that support the Company’s business goals.
Essential Functions:
Essential Job Function
% of Time on Function
Create and maintain enterprise and solution architecture for AI products, platforms, and shared AI capabilities
30%
Design API integrations, data flows, service interactions, and architectural patterns connecting AI solutions to the enterprise ecosystem
25%
Partner with AI product, engineering, data, infrastructure, cybersecurity, and business teams to define requirements and provide architectural guidance
20%
Establish architecture standards, governance, documentation, security requirements, and non\-functional requirements for enterprise AI solutions
15%
Evaluate emerging AI technologies, conversational AI platforms, model services, and reusable enterprise capabilities
10%
Total
100%
Job Requirements:
- Bachelor’s degree in Computer Science or a related discipline; Master’s degree preferred.
- Minimum of 12 years of experience in systems architecture, solution architecture, systems integration, software engineering, or an equivalent combination of education and work experience.
- Experience providing technology direction and architectural guidance for enterprise\-wide solutions.
- Experience designing architectures for AI\-powered applications, generative AI, machine learning, or conversational AI solutions.
- Familiarity with large language model deployment patterns, including managed model services, privately hosted models, retrieval\-augmented generation, grounding, inference, and prompt management.
- Experience integrating conversational AI platforms with enterprise applications, digital channels, voice platforms, contact center technologies, or customer data.
- Expertise in API architecture, web services, microservices, event\-driven architecture, system integrations, and enterprise integration best practices.
- Strong understanding of data architecture fundamentals, including data access, modeling, movement, quality, lineage, privacy, and governance.
- Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud and their AI, data, and integration services.
- Understanding of enterprise architecture principles, including scalability, reliability, security, privacy, and observability.
- Familiarity with responsible AI, AI governance, model lifecycle management, and AI risk management.
- Ability to create architecture diagrams, data\-flow diagrams, integration specifications, technical standards, and architecture roadmaps.
- Solid understanding of the software development lifecycle, architecture governance, change control, and problem management processes.
- Excellent communication, leadership, collaboration, and analytical skills, with the ability to work effectively with technical teams, business stakeholders, and all levels of management.
- Strong influencing, problem\-solving, team\-building, and mentoring skills.
- Proactive, results\-oriented, and capable of operating as a senior technical subject matter expert.
Minimum Physical Requirements:
The physical demands described represent those that must be met by an employee to successfully perform the essential functions of this position. Reasonable accommodations may be made to enable individuals with disabilities to perform the functions of the position for which they work. While performing the duties of this position, the employee is regularly required to listen, talk, and hear. The employee frequently is required to use hands or fingers, handle or feel objects, tools, or controls while executing tasks like working on a computer or talking on the telephone. The employee is occasionally required to stand; walk; sit; and reach with hands and arms. The employee must occasionally lift and/or move up to 15 pounds. Specific vision abilities required by this position include close vision, distance vision, and the ability to adjust focus. The noise level in the work environment is usually moderate to low.
This job description is intended to provide guidelines for job expectations and the employee's ability to perform the position described. It is not intended to be construed as an exhaustive list of all functions, responsibilities, skills and abilities. Additional functions and requirements may be assigned by supervisors as deemed appropriate.
Salary Range (Norwalk, CT): $125,932\.60 \- $167,910\.14
Annual Bonus Potential: 10%
HomeServe USA is an equal opportunity employer.
\#HUSA \#LI\-NM1 \#LI\-ONSITE
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
Salary Context
This $125K-$167K range is below the median 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 HomeServe USA, 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 ($146K) sits 32% below the category median. Disclosed range: $125K to $167K.
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.
HomeServe USA AI Hiring
HomeServe USA has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Norwalk, CT, US. Compensation range: $167K - $167K.
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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