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About This Role
Generative AI \& Machine Learning \| Google Cloud Platform \| Federal Program
Location: Remote (United States) — occasional travel may be required
Employment Type: Full\-Time (Contract)
Rate: $100 – $150 per hour (commensurate with experience)
Level: Mid\-Senior / SME (3–6\+ years)
Citizenship: U.S. Citizenship required — no exceptions, no visa sponsorship, no C2C through third\-party visa holders
Clearance: Must be able to obtain and maintain Minimum Background Investigation (MBI)
About the Role
Lanthos Tech is seeking a GCP AI Engineer (SME) to design, build, and scale a production\-grade AI research system on Google Cloud Platform.
This role sits at the intersection of applied AI, software engineering, and federal compliance. You will build Retrieval\-Augmented Generation (RAG) pipelines, deploy models through Vertex AI, and integrate Gemini APIs — all within a FedRAMP\-authorized, ATO\-governed cloud environment. It suits an engineer who is equally comfortable writing production Python, architecting cloud infrastructure, and reasoning about model behavior, latency, cost, and auditability.
What You'll Do
- Design, build, and deploy machine learning and generative AI pipelines on Vertex AI, including model training, fine\-tuning, evaluation, and serving.
- Build RAG architectures using vector search (e.g., Vertex AI Vector Search) to ground LLM outputs .
- Integrate Gemini APIs and other foundation models into mission applications and internal analyst tooling.
- Develop agentic workflows and multi\-step orchestration using frameworks such as LangChain, LangGraph, or CrewAI.
- Build and deploy agents using Google's Agent Development Kit (ADK) and integrate them with Gemini Enterprise for enterprise\-grade deployment, access control, and governance.
- Operate agents within managed runtime environments (e.g., Vertex AI Agent Engine), handling session state, tool\-calling, and scaling of long\-running agent workloads.
- Architect and maintain scalable, secure cloud infrastructure across GCP services (BigQuery, Cloud Run, Pub/Sub, Cloud Functions, GKE), using FedRAMP\-authorized services and government\-approved regions.
- Implement MLOps practices: CI/CD for models, automated retraining, monitoring, drift detection, evaluation harnesses, and rollback strategies.
- Design systems that meet federal security and privacy obligations.
- Support the Authority to Operate (ATO) process by producing architecture artifacts, data\-flow diagrams, and control evidence in partnership with the security and compliance lead.
- Write clean, well\-tested, production\-quality Python.
- Collaborate with data scientists, product managers, platform engineers, and government stakeholders to translate mission requirements into technical solutions.
- Monitor and optimize model performance, latency, and inference cost in production.
- Document architecture decisions, pipelines, model cards, and operational runbooks for maintainability and government handover.
Required Technical Skills
Programming
- Strong proficiency in Python; working knowledge of JavaScript or TypeScript is a plus.
GCP \& AI Tools
- Hands\-on experience with Vertex AI (training, pipelines, endpoints, model registry).
- Experience with Gemini APIs and other GCP AI/ML services.
- Proficiency with BigQuery for data warehousing and analytics workloads.
GenAI \& Frameworks
- Solid understanding of RAG architectures and vector database implementations.
- Experience with agentic orchestration frameworks: LangChain, LangGraph, CrewAI, or equivalent.
- Hands\-on experience with Google's Agent Development Kit (ADK) for building, testing, and deploying agents.
- Familiarity with Gemini Enterprise for enterprise agent deployment, access control, and governance.
- Experience operating agents in a managed agent runtime (e.g., Vertex AI Agent Engine), including session/state management and tool orchestration at scale.
- Familiarity with prompt engineering, embeddings, and LLM evaluation techniques (groundedness, hallucination detection, retrieval quality).
ML Operations
- Demonstrated ability to scale, monitor, and automate ML pipelines in production.
- Experience with containerization (Docker) and orchestration (Kubernetes / GKE).
- Familiarity with CI/CD tooling (Cloud Build, GitHub Actions, or similar).
Federal Eligibility Requirements
These are contractual requirements and are not negotiable:
- Must be a U.S. Citizen. Documentation of citizenship will be required. Permanent residents, visa holders, and dual\-status candidates requiring sponsorship are not eligible for this engagement.
- Must reside in and perform all work from within the United States. Offshore or overseas work is prohibited.
- Must be able to pass an background investigation (Public Trust / MBI), including fingerprinting, and a favorable federal tax compliance check (filed and current on all tax obligations).
- Must complete required federal security, privacy, and Safeguards (Pub 1075\) training prior to system access.
Qualifications \& Education
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience. Master's or PhD preferred for senior/SME\-level scope.
- 3–6\+ years of software development or production AI/ML engineering experience.
- Prior experience deploying AI/ML systems in a production, end\-user\-facing environment strongly preferred.
- Prior federal, public sector, or other regulated\-industry delivery experience is a significant advantage.
Preferred Certifications
- Google Cloud Professional Machine Learning Engineer (recommended).
- Google Cloud Professional Cloud Architect or Professional Data Engineer (a plus).
Nice to Have
- Experience with multi\-agent systems or autonomous agent design.
- Prior work in a FedRAMP or ATO\-governed cloud environment.
- Familiarity with Section 508 / accessibility requirements for government\-facing applications.
- Experience handling PII, FTI, or other sensitive data under formal safeguards.
- Contributions to open\-source ML/AI tooling.
- Familiarity with cost optimization strategies for large\-scale inference workloads.
Compensation \& Engagement Details
- $100 – $150 per hour, based on experience, certifications, and depth of GCP AI delivery history.
- Full\-time contract engagement supporting a federal program.
- 100% remote within the United States.
- Professional development and certification support available.
Interview Process
- Recruiter screen — eligibility, experience, and availability.
- Round 1: Technical interview (60 minutes) — GCP AI/ML depth, RAG and agent architecture, hands\-on scenarios.
- Round 2: Leadership interview — delivery experience, collaboration, and federal program fit.
- Additional round may be scheduled on a need basis.
- Onboarding — citizenship verification, background investigation, and tax compliance check.
*Lanthos Tech is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. The U.S. citizenship and background investigation requirements stated above are imposed by the terms of the federal contract this position supports.*
Pay: $90\.00 \- $150\.00 per hour
Application Question(s):
- Are you a U.S Citizen ?
Work Location: Remote
Salary Context
This $187K-$312K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At DATA PULSE TECH AI LLC, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($249K) sits 16% above the category median. Disclosed range: $187K to $312K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
DATA PULSE TECH AI LLC AI Hiring
DATA PULSE TECH AI LLC has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $187K - $312K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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