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About This Role
AI Architect
We are seeking an AI Architect to embed with our largest, most complex enterprise clients and lead the deployment of cutting-edge Salesforce Agentforce solutions. This high-impact role combines deep hands-on AI systems engineering, enterprise platform architecture, and high-visibility client leadership.
You will bridge the gap between business strategy and autonomous AI execution. You will "command the room" with C-suite stakeholders, dive deep into code and data pipelines, and build scalable, production-grade Agentforce architectures that deliver immediate, measurable business transformation.
Role Responsibilities:
- Field Engineering & Autonomous AI Execution + Embed directly with client engineering and business leadership to rapidly prototype, architect, and deploy custom Agentforce autonomous agents, topics, actions, and guardrails. + Design and implement complex agent workflows utilizing Einstein 1 Platform, Data Cloud, and custom API actions (Apex, Flow, MuleSoft, and external REST endpoints). + Establish robust evaluation, testing, and observability frameworks (evals) to monitor agent accuracy, hallucination rates, latency, and business performance in production. + Unblock critical deployment bottlenecks in real-time, functioning as the high-velocity engineering engine on $2M+ strategic engagements.
- Enterprise Architecture & Data Strategy + Own the end-to-end technical architecture for multi-cloud, AI-driven implementations, ensuring enterprise-grade scalability, security, context grounding, and compliance. + Architect real-time data pipelines using Salesforce Data 360 to power Agentforce context engines, unifying disparate data lakes (Snowflake, Databricks, BigQuery) with CRM data. + Produce clear architectural artifacts, including system sequence diagrams, agent action flows, data schemas, and security/governance frameworks. + Evaluate emerging AI/LLM models, prompt engineering patterns, and Agentforce features to build reusable assets, agent templates, and internal accelerators.
- Strategic Client Leadership & Practice Growth + Serve as a trusted technical advisor to C-suite, VP, and Enterprise Architecture stakeholders, framing complex AI paradigms into clear business outcomes. + Lead high-stakes discovery sessions, live agent demonstrations, and architectural design reviews. + Partner with Delivery Leadership and Account Executives to scope, estimate, and technically validate high-value Agentforce proposals. + Share field-tested insights and deployment patterns with internal teams to help mature our Agentforce Center of Excellence.
Experience/Skills Required:
- Production AI & Salesforce Experience: 6+ years of solution architecture experience on the Salesforce platform, with explicit, hands-on experience building and deploying Salesforce Agentforce, Einstein Copilot, or advanced Data Cloud implementations.
- Forward Deployed / Consulting Agility: 5+ years in client-facing technical consulting or FDE roles, with a proven track record of shipping production code and systems under tight timelines on $2M+ accounts.
- Hands-on Development Skills: Strong proficiency in Apex, Flow, LWC, REST/SOAP APIs, and modern programmatic integration patterns required to trigger agent actions across external stacks.
- Data & Grounding Architectures: Deep understanding of Data Cloud, zero-copy architecture, vector databases, RAG (Retrieval-Augmented Generation), and enterprise data ingestion strategies.
- AI Safety & Governance: Demonstrated experience implementing agent guardrails, prompt defense, audit logging, and enterprise compliance frameworks.
- Executive Communication: Exceptional ability to simplify complex generative AI concepts, LLM mechanics, and agentic workflows for executive audiences.
- Work Authorization & Travel: Full-time permanent US work authorization. Willing and able to travel 50%+ to client sites as required for high-velocity field engagements.
- Education: Bachelor’s degree in Computer Science, Software Engineering, or equivalent practical experience.
Preferred Certifications:
- Salesforce: Salesforce Certified AI Specialist, Salesforce Certified Data Cloud Consultant, Application Architect, or System Architect.
- Data Platforms: Snowflake Certified Core/Advanced, Databricks Certified Data Engineer, or MuleSoft Integration Architect.
- Enterprise Architecture & Cloud: AWS Certified Solutions Architect (Professional), Azure Solutions Architect Expert, Google Cloud Professional Cloud Architect, or TOGAF.
Why Coastal, and what we offer:
- Flexible working hours with an emphasis on a life-work balance (in that order!)
- Remote friendly; plus company office locations in Palm Coast, FL; Atlanta, GA; Tysons, VA & Lexington, KY
- Unlimited Paid Time Off (RTO), 401K with Company Match, and Medical, Vision, & Dental coverage
- Competitive quarterly bonus opportunities
- Continuing education and certification reimbursements, specifically within the Salesforce and Snowflake ecosystems; plus occasional in-house competitions with spot bonuses
- A flexible and fun team culture! We value transparency, support, flexibility, growth, teamwork, fun, and so much more
- Frequent team and culture activities, virtual & in-person, including Lunch and Learns, Happy Hours, team-building events
- Monthly All-Hands calls to bring the company together, and an open-door leadership policy with access to mentorship and guidance
- Opportunities for accelerated growth, networking, and career guidance and support
- Trust, transparency and respect across all levels of the company
Coastal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.
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 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At Coastal, 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 $230,000 based on 212 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 $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Coastal AI Hiring
Coastal has 1 open AI role right now. They're hiring across AI Architect. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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).
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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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