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About This Role
We are looking for an Application Architect with strong AI engineering experience to design and build intelligent, agentic applications on Google Cloud Platform. This role sits within an application engineering team and focuses on architecting AI\-enabled systems using the Google Agentic Development Kit (ADK), Gemini, and Vertex AI — integrated into enterprise Java/Python backends and cloud\-native microservices. You are equally comfortable defining application architecture, designing agentic workflows, writing production\-quality code, and translating AI capabilities into practical, mission\-aligned solutions for federal stakeholders.
This role is remote with a preference for candidates located in Virginia, Maryland, or Washington DC.
Key Responsibilities* Architect and implement AI\-enabled application systems on GCP, with a focus on agentic workflows using Google ADK and Gemini Pro.
- Design human\-in\-the\-loop agentic systems — defining agent roles, tool use, orchestration patterns, and guardrails for responsible, auditable AI behavior.
- Integrate AI/ML capabilities (Vertex AI, Gemini APIs, embeddings, RAG) into enterprise Java and Python applications via well\-designed APIs and microservices.
- Lead application\-layer design decisions: data flow, context management, session handling, and state management within agentic architectures.
- Collaborate with Data Engineers (BigQuery, Dataform) and Cloud Architects to ensure AI application solutions are grounded in reliable, governed data.
- Conduct architectural reviews, define coding standards for AI\-integrated applications, and mentor engineers on agentic design patterns.
- Evaluate AI use cases for feasibility, risk, and mission fit; prototype and validate approaches before committing to full builds.
- Contribute to responsible AI practices: explainability, human oversight, auditability, and alignment with federal AI governance requirements.
- Stay current on the Google AI ecosystem (Gemini, ADK, Vertex AI Agent Builder) and inform team and leadership on strategic direction.
Requirements
Required Qualifications* 5–8 years of software or application engineering experience, with demonstrated focus on AI\-integrated or intelligent application design.
- Hands\-on experience with Google ADK or comparable agentic frameworks (LangGraph, LangChain, AutoGen); Google ADK strongly preferred.
- Proficiency in Python for AI/ML integration; Java experience a plus in application team context.
- Experience integrating LLM APIs (Gemini, OpenAI, or equivalent) into production application workflows.
- Solid understanding of agentic design patterns: tool use, multi\-agent orchestration, retrieval\-augmented generation (RAG), memory and context management.
- Experience with GCP services: Vertex AI, Cloud Run, GKE, BigQuery, Pub/Sub.
- Familiarity with REST API design, microservices architecture, and CI/CD pipelines (Harness preferred).
- Understanding of responsible AI principles: human\-in\-the\-loop design, auditability, bias awareness, and federal AI governance.
Desired Qualifications* Experience with Vertex AI Agent Builder, Gemini Code Assist, or Gemini CLI in a development workflow context.
- Familiarity with GCP\-native data tooling: BigQuery, Dataform, Looker.
- Experience on federal or large\-scale enterprise modernization programs.
- Exposure to FedRAMP/FISMA requirements and security\-compliant AI deployment practices.
- Experience with DevSecOps pipelines (Checkmarx, Invicti, or equivalent SAST/DAST tooling).
Additional Information* Successful completion of a client\-required background investigation and suitability determination will be required.
- The ability to obtain and maintain a federal security clearance may be required based on engagement.
- Bachelor's degree in Computer Science, Software Engineering, or a related field; advanced degree a plus.
- Google Cloud Professional Cloud Architect or Professional Machine Learning Engineer certification preferred.
- Security\+ desirable.
Benefits
- Health Care Plan (Medical, Dental \& Vision)
- Retirement Plan (401k, IRA)
- Life Insurance (Basic, Voluntary \& AD\&D)
- Paid Time Off (Vacation, Sick \& Public Holidays)
- Family Leave (Maternity, Paternity)
- Short Term \& Long Term Disability
- Training \& Development
- Work From Home
- Wellness Resources
- Employee Bonus Programs
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Northramp, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Northramp AI Hiring
Northramp has 2 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Based in Washington, DC, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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