GCP AI Engineer — Subject Matter Expert (SME)

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