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About This Role
Technical Project Manager (Google Cloud \& AI Solutions)
United States
Remote
Fulltime or Contract 1099 Salary
- FTE Salary OR Hourly/Contract (1099\)
About GTS
Global Technology Solutions, Inc.is a US\-based company that specializes in CCaaS, AI \& ML and Cloud Solutions. We are a Google Cloud Partner; Gold Partner with Genesys; Advance Partner with AWS, and help with implementation, consulting, managed services, and product development. Our operation is fully remote, and employees do not have to go to the office. We are building a customer\-centric agile team to deliver projects for our customers with a higher ROI.
Here are some of the exciting initiatives we are working on:
- Creating AI driven modern and highly scalable CCaaS solutions for our customers
- Designing and developing amazing user experiences across data visualization and analytic
- Building omni\-channel solutions to drive customer engagement.
- Creating outcome driven products to solve customer pain points.
We strongly believe that empowered people are the key to building a great company. Our mission as a company is to create an environment where our people are MOTIVATED and THRIVE.
Role Overview
At GTS, the Technical Project Manager (TPM) is the primary customer focal point responsible for managing the technical implementation of system and cloud solutions for GTS customers. This role focuses on successful project planning, technical coordination, and delivery of customer\-approved statements of work—on time, in scope, and within budget.
While GTS works across multiple platforms, this position specifically targets candidates with experience leading Google Cloud\-related projects.
About You: A successful TPM must be a self\-starter who can manage several concurrent medium\-to\-large projects with differing technical workstreams. You will bridge the gap between business requirements and technical execution, working effectively with Business Analysts, Cloud Architects, AI Developers, Account Executives, and customer technical teams.
We are seeking candidates that are engaging and proactive that are passionate about creating best in class client experiences that can lead teams through the complex project technical and non\-technical challenges, while maintaining awareness of Google Cloud’s latest innovations as relevant to our business.
Logistics: Occasional travel and after\-hours work are required for this position to support critical deployment windows, otherwise this position is remote.
Key Responsibilities
Technical Project Delivery \& Execution
- Manage End\-to\-End Implementation: Drive multiple medium\-to\-large technical projects featuring conversational AI, cloud contact center, and workspace solutions.
- Translate \& Align: Bridge the gap between business stakeholders and technical teams; translate complex business requirements into technical milestones and project tasks.
- Methodology Champions: Present and execute the GTS Project Implementation Methodology, tracking deliverables diligently while offering feedback to the PMO to optimize technical deployment processes.
- Documentation \& Governance: Create and maintain comprehensive project plans, including Technical Work Plans/Task Lists, API/Integration Milestones, Risk Management Plans, and Testing/UAT Plans.
- Lifecycle Management: Oversee discovery, configuration, build, testing, and deployment phases, ensuring that Google Cloud and AI solutions align with customer architectural standards.
Client \& Internal Collaboration
- Primary Point of Contact: Serve as the main technical and operational interface for the client throughout the project lifecycle.
- Stakeholder Communication: Effectively communicate project status, technical roadblocks, scope changes, and budget updates to both technical teams and executive leadership.
- Resource Coordination: Collaborate with cross\-functional internal and external teams, including Pre\-Sales Engineers, Support, Resource Managers, and Finance.
- Risk Mitigation: Proactively identify technical dependencies and risks; escalate issues swiftly through the recommended path to prevent project slippage.
- System Integrity: Update internal GTS tracking systems, collaboration tools, and resource booking plans weekly to ensure accurate financial and operational reporting.
Qualifications \& Requirements
Professional Experience
- 4–7 years of Project Management experience, ideally within a professional services, consulting, or IT systems integration environment.
- 3\+ years of direct experience deploying Google Cloud Data and AI solutions (experience with Gemini Enterprise Customer Experience (GECX) or general Contact Center as a Service (CCaaS) solutions highly preferred).
- Proven track record of managing technical project teams (Architects, Developers, QA) without needing direct supervisory authority.
Technical Expertise (Google Ecosystem Focus)
- Demonstrated experience leading Google Cloud/Google Workspace projects.
- Hands\-on familiarity or strong PM oversight of the following capabilities:
+ Conversational Agents: Managing the deployment of virtual agents, intents, and fulfillment webhooks.
+ Google Enterprise Customer Experience (GECX) \& CX Agent Studio: Overseeing the implementation of modern, AI\-driven customer journeys.
+ Gemini Enterprise App and Agent Platform: Driving projects that leverage generative AI for enterprise productivity or customer support automation.
- Solid overall proficiency in Google Workspace and Microsoft 365 collaboration tools.
Education \& Certifications
- Education: Bachelor’s degree in computer science, Management Information Systems (MIS), Engineering, Project Management, or a related technical field (commensurate experience or technical certifications without a degree will be considered).
- Certifications (Preferred):
+ PMP Certification (or an active, verifiable plan to achieve it).
+ Certified Scrum Master (CSM) or equivalent Agile certification (highly desired for iterative AI/software deployments).
+ Any Google Cloud certifications (e.g., Google Cloud Digital Leader, Professional Cloud Project Manager) are a major plus.
Salary Context
This $120K-$160K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Global Technology Solutions Inc, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $120K to $160K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Global Technology Solutions Inc AI Hiring
Global Technology Solutions Inc has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $160K - $160K.
Location Context
AI roles in Austin pay a median of $214,343 across 87 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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