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About This Role
AI Architect, Principal Frontline AI Engineer \- Healthcare
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About Brillio:
Brillio is one of the fastest growing digital technology service providers and a partner of choice for many Fortune 1000 companies seeking to turn disruption into a competitive advantage through innovative digital adoption. Brillio, renowned for its world\-class professionals, referred to as "Brillians", distinguishes itself through their capacity to seamlessly integrate cutting\-edge digital and design thinking skills with an unwavering dedication to client satisfaction.
Brillio takes pride in its status as an employer of choice, consistently attracting the most exceptional and talented individuals due to its unwavering emphasis on contemporary, groundbreaking technologies, and exclusive digital projects. Brillio's relentless commitment to providing an exceptional experience to its Brillians and nurturing their full potential consistently garners them the Great Place to Work® certification year after year. Role Brief:
We are seeking an AI Architect – Healthcare AI Transformation to partner with one of the largest healthcare payers in the United States to design and scale AI\-driven transformation initiatives. This role operates at the intersection of AI architecture, healthcare operations, enterprise technology, and business transformation.
The AI Architect will work directly with executive leaders, business stakeholders, clinical and operational teams, and engineering organizations to identify high\-value AI opportunities, define enterprise AI strategies, architect solutions, and guide implementation from concept through production.
This role goes beyond traditional architecture. The successful candidate will combine deep AI engineering expertise with healthcare domain understanding to redesign business processes using Generative AI, Agentic AI, LLMs, and intelligent automation while enabling organizations to become AI\-native.###### Key Responsibilities:
- Partner directly with healthcare executives, business stakeholders, clinical leaders, and technology teams to identify AI transformation opportunities and define AI\-driven solutions.
- Translate complex healthcare business challenges into scalable AI architectures, technical strategies, and implementation roadmaps.
- Design and architect enterprise AI solutions leveraging Generative AI, Large Language Models (LLMs), AI agents, Retrieval\-Augmented Generation (RAG), and intelligent automation.
- Define AI solution patterns including multi\-agent architectures, workflow orchestration, knowledge retrieval systems, and enterprise AI integrations.
- Lead AI solution development from discovery and architecture through prototyping, production deployment, and optimization.
- Design AI\-enabled workflows to transform healthcare operations including claims processing, prior authorization, care management, provider operations, member services, and payment integrity.
- Build and guide implementation of AI applications using modern AI frameworks, cloud platforms, APIs, and enterprise data ecosystems.
- Collaborate with engineering, data science, product, security, and governance teams to ensure scalable, secure, and responsible AI adoption.
- Establish AI architecture standards, best practices, reusable frameworks, and accelerators for enterprise AI delivery.
- Support AI governance practices including model monitoring, explainability, security, compliance, and responsible AI implementation.
- Lead technical workshops, architecture reviews, executive presentations, and solution demonstrations with client stakeholders.
- Mentor engineering teams and enable business teams to adopt AI tools, workflows, and AI\-native operating models.
###### Required Skills \& Experience:
- 10\+ years of experience in software engineering, AI engineering, machine learning, enterprise architecture, or digital transformation.
- Hands\-on experience designing and implementing Generative AI and AI/ML solutions in enterprise environments.
- Strong understanding of Large Language Models (LLMs), AI agents, prompt engineering, RAG architectures, and AI application patterns.
- Experience architecting and delivering production\-grade AI solutions from concept through deployment.
- Strong software engineering background with proficiency in Python, APIs, microservices, and cloud\-based application development.
- Experience designing AI workflows, orchestration patterns, tool integrations, and enterprise AI architectures.
- Experience working with cloud AI platforms such as AWS, Microsoft Azure, or Google Cloud Platform.
- Ability to translate business requirements into technical architectures and communicate solutions to both technical and executive audiences.
- Experience working directly with business stakeholders, clients, or cross\-functional teams in a consulting or enterprise environment.
- Experience with AI governance, security, monitoring, evaluation frameworks, and responsible AI practices.
- Healthcare industry experience with understanding of payer/provider workflows, healthcare operations, and regulated environments.
- Knowledge of healthcare compliance considerations including HIPAA and healthcare data privacy.
###### Good to Have:
- Experience as an AI Architect, Forward Deployed Engineer, Solutions Architect, or Consulting Architect.
- Experience building agentic AI systems using frameworks such as LangGraph, LangChain, CrewAI, AutoGen, or Semantic Kernel.
- Experience with Claude (Anthropic), OpenAI, Gemini, or other enterprise LLM platforms.
- Experience developing healthcare AI solutions for:
o Claims processing and adjudication
o Prior authorization
o Utilization management
o Care management
o Member/provider engagement
o Revenue cycle management
o Payment integrity
- Familiarity with healthcare data standards including HL7, FHIR, ICD\-10, CPT, and claims data structures.
- Experience with Retrieval\-Augmented Generation (RAG), vector databases, embeddings, and enterprise search solutions.
- Experience integrating AI solutions with enterprise platforms such as Salesforce, ServiceNow, Epic, or healthcare workflow systems.
- Experience creating reusable AI accelerators, reference architectures, and enterprise AI frameworks.
- Experience supporting AI adoption, enablement, and training for business and engineering teams.
- Experience working in highly regulated industries where security, governance, and auditability are required.
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Salary: 200,000\-300,000 USD per year salary
Salary Context
This $200K-$300K range is above the 75th percentile for AI Architect roles in our dataset (median: $197K across 33 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At Brillio LLC, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $254,798 based on 67 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. Disclosed range: $200K to $300K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Brillio LLC AI Hiring
Brillio LLC has 1 open AI role right now. They're hiring across AI Architect. Based in Jersey City, NJ, US. Compensation range: $300K - $300K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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