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About This Role
As a Principal Architect for AI-Native Application Development, you will define and lead the architecture for AI- assisted and agent-enabled software delivery. You will establish Claude Code and other approved AI harnesses as governed engineering capabilities, wired into enterprise knowledge, repositories, JIRA, testing, CI/CD, and operational feedback.
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: 12+ Years
Work Authorization: Must be authorized to work in the US without current or future visa sponsorship
As a Principal Architect for AI-Native Application Development, you will define and lead the architecture for AI-
assisted and agent-enabled software delivery. You will establish Claude Code and other approved AI harnesses
as governed engineering capabilities, wired into enterprise knowledge, repositories, JIRA, testing, CI/CD, and
operational feedback.
This role is responsible for taking Application Development (AD) from harness-driven coding to closed-loop,
agent-run pipelines. We are looking for an architect with a strong background in cloud-native and DevSecOps
solutions who can lead architects and engineers across multiple delivery teams.
Your Role Responsibilities and Duties:
- AI-Native SDLC Architecture: Define the target architecture and roadmap across story analysis, design,
coding, testing, review, and release, and set clear boundaries between assisted and autonomous work.
- Engineering Knowledge & Context: Govern the engineering knowledge foundation (JIRA, code, wikis,
tests, and CI/CD evidence) with RAG, lineage, and citation standards.
- Developer Agent Architecture: Design and govern agents for design, code generation, code review,
unit/functional/regression testing, and pipeline remediation.
- MCP & Tool Integration: Define Model Context Protocol (MCP) standards for JIRA, GitHub/GitLab,
SonarQube, CI/CD, and security tools, with safe read/write action tiers.
- Quality, Security & Governance: Set evaluation and release gates; guard against secret exposure,
insecure code, and prompt injection; and define human-review and kill-switch patterns.
- Adoption & Leadership: Build application-readiness roadmaps, mentor AI agent engineers, and tie
adoption to measurable speed, quality, and cost outcomes. Required Skills and Qualifications:
- 12+ years in software engineering, architecture, platform engineering, or application modernization.
- 5+ years designing cloud-native, DevSecOps, or large-scale development solutions.
- Experience leading architects and engineers across multiple teams.
- Practical experience with generative AI, developer assistants, or AI agents.
- Expertise in enterprise architecture and modern application development.
- Strong experience with APIs, microservices, automated testing, CI/CD, and DevSecOps.
- Working knowledge of Claude Code or similar AI coding tools.
- Knowledge of MCP or similar enterprise tool integrations.
- Experience with GitHub/GitLab, Jira, and code-quality and CI/CD platforms.
Preferred Qualifications:
- Degree in Computer Science, Engineering, or Information Systems.
- Experience with LangGraph, LangChain, ADK, Semantic Kernel, or AutoGen.
- Experience with GitHub Copilot, Gemini, Devin, or other enterprise AI platforms.
- Certification in cloud, security, DevOps, or SRE.
- Experience in telecom or large enterprises.
Mandatory Skills:
Software Architecture, Cloud-Native & Microservices, DevSecOps & CI/CD, Claude Code (or comparable AI coding harness), MCP, GitHub/GitLab, JIRA
What We Offer:
- Bootstrapped, financially stable company with a high pre-money valuation.
- Above-industry compensation.
- Additional compensation for renewals and pilot project execution.
- Attractive business development incentives.
- Opportunity to work with industry experts on data-driven strategy.
- Opportunities to help build the company, grow professionally, and develop your personal brand.
- Empathetic, results-focused culture that values excellence, mentoring, and continuous learning.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Saarthee, 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
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/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 9,653 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).
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 9,653 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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