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About This Role
We are looking for an Enterprise AI Architect to join our growing team and lead the strategy and architecture for secure, governed AI agents across application development and maintenance operations.
Mail us at: [email protected]
- United States (Remote)
- Remote – open to US-based candidates only
Why Join Us
About Saarthee:
Saarthee is a Global Strategy, Analytics, Technology and AI consulting company, where our passion for helping others fuels our approach and our products and solutions. Our diverse and global team work with one objective in mind: Our Customers’ Success. At Saarthee, we are passionate about guiding organizations towards insights fueled success. That’s why we call ourselves Saarthee–inspired by the Sanskrit word ‘Saarthi’, which means charioteer, trusted guide, or companion. Cofounded in 2015 by Mrinal Prasad and Shikha Miglani, Saarthee already encompasses all the components of Data Analytics consulting. Saarthee is based out of Philadelphia, USA with office in UK and India.
Position Summary
Location: United States (Remote)
Work Mode: Remote – open to US-based candidates only
Min-Max Experience: 15+ Years
Work Authorization: Must be authorized to work in the US without current or future visa sponsorship
As an Enterprise AI Architect, you will lead the evolution of Application Development (AD) and Application Maintenance Services (AMS) from AI-assisted work to governed, agent-enabled operations. You will own the single shared foundation of enterprise knowledge, AI harnesses, agents, Model Context Protocol (MCP) integrations, and governance that powers both the AD loop and the AMS loop.
This role is responsible for defining and driving the maturity roadmap that moves both AD and AMS from harness-driven delivery to enterprise-scaled autonomy. We are looking for a seasoned architect with deep enterprise architecture experience and a proven record of leading production-grade AI, data, and automation initiatives across engineering, operations, security, and risk teams.
Your Role Responsibilities and Duties
- Strategy & Maturity Roadmap: Define the AD/AMS AI strategy, set entry and exit criteria for each maturity level, and segment use cases by value, risk, and readiness.
- Shared AI Foundation: Own the reference architecture and the Enterprise Knowledge Base (code, JIRA, ServiceNow, runbooks, and logs), with governed RAG, permissions, and lineage.
- Harness & Agent Architecture: Set enterprise standards for approved harnesses, prompts, skills, sub-agents, and multi-agent patterns, and define clear boundaries between assisted and autonomous work.
- MCP & Enterprise Integration: Establish the MCP registry, tool certification process, and action categories ranging from read-only to approval-required.
- Closed-Loop Engineering & Operations: Connect tests, reviews, pipelines, telemetry, and incidents to agent workflows so that AD and AMS continuously learn from each other.
- Governance & Responsible AI: Define risk classification, human-in-the-loop checkpoints, kill switches, rollback, and audit requirements, ensuring autonomy is earned, not assumed.
Required Skills and Qualifications
- 15+ years of experience in technology, engineering, architecture, or transformation roles, including 8+ years in enterprise architecture, engineering platforms, cloud transformation, or large-scale delivery transformation.
- Proven leadership of production-grade AI, data, automation, or intelligent-platform initiatives.
- Experience leading cross-functional programs across engineering, operations, security, data, and risk teams.
- Enterprise architecture expertise across application, data, integration, security, cloud, and operating-model domains.
- Strong grounding in LLMs, retrieval-augmented generation (RAG), embeddings, tool calling, and multi-agent orchestration.
- Knowledge of AI harnesses (Claude Code, GitHub Copilot, Gemini, Devin) and agent frameworks (LangGraph, ADK, Semantic Kernel).
- Solid foundations in software engineering, DevSecOps, CI/CD, observability, and SRE.
- Ability to brief executive and technical audiences on complex AI and architecture decisions.
Preferred Qualifications
- Bachelor’s or master’s degree in Computer Science, Engineering, or a related field; relevant cloud, security, SRE, or AI certification.
- Experience with GCP Vertex AI / Gemini; familiarity with Azure AI or AWS Bedrock is a plus.
- Experience in telecom, financial services, or another large-scale regulated environment.
Mandatory Skills
Enterprise Architecture, LLMs & RAG, Multi-Agent Orchestration, AI Harnesses (Claude Code / GitHub Copilot), MCP, DevSecOps & CI/CD, AI Governance.
What We Offer
- Bootstrapped and financially stable with high pre-money valuation.
- Above-industry remuneration.
- Additional compensation tied to renewals and pilot project execution.
- Additional lucrative business development compensation.
- Chance to work closely with industry experts driving strategy with data and analytics.
- Firm-building opportunities that provide a platform for holistic professional development, growth, and branding.
- An empathetic, excellence-driven, and results-focused organization that believes in mentoring and growing a team with a constant emphasis on learning.
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 Saarthee, 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.
Saarthee AI Hiring
Saarthee has 4 open AI roles right now. They're hiring across AI Agent Developer, AI/ML Engineer, 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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