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About This Role
About The Company
We are a rapidly growing global workforce and business process outsourcing (BPO) company providing customer service, collections, recruiting, healthcare support, finance, back\-office operations, and workforce solutions to clients worldwide.
As businesses continue to explore how AI can improve performance and efficiency, we are looking for a BPO AI Solutions Architect who can help identify opportunities, solve operational challenges, and design practical AI\-driven solutions that create measurable business value.
This is not a traditional software engineering role.
We are looking for someone who understands business operations, can engage directly with clients and stakeholders, and knows how to bridge the gap between operational challenges and AI\-powered solutions.
About the Role
As a BPO AI Solutions Architect, you will work closely with prospects, clients, and internal teams to evaluate business processes, identify inefficiencies, and recommend AI and automation solutions that improve productivity, reduce costs, enhance customer experiences, and support business growth.
You will participate in discovery calls, operational assessments, solution design discussions, and implementation planning. The ideal candidate is equally comfortable speaking with executives, operations leaders, customer service managers, recruiting teams, and technical stakeholders.
This role combines elements of:
- AI Solutions Consulting
- Business Process Analysis
- Operational Improvement
- Solutions Engineering
- AI Automation Strategy
Key Responsibilities
- Participate in sales, discovery, and client consultation meetings.
- Analyze operational workflows and identify opportunities for AI and automation.
- Conduct process mapping and workflow assessments.
- Design AI\-powered solutions for customer service, collections, recruiting, workforce management, quality assurance, reporting, and back\-office operations.
- Develop business cases and ROI estimates for proposed solutions.
- Create implementation roadmaps and project plans.
- Collaborate with operations, recruiting, payroll, IT, and client success teams.
- Support the implementation and adoption of AI initiatives.
- Evaluate emerging AI technologies and recommend practical business applications.
- Assist in developing internal and client\-facing AI solutions.
Why Join Us?
- Work with clients across multiple industries and global markets.
- Help shape the future of AI adoption within the BPO industry.
- Influence business transformation initiatives from strategy through execution.
- Join a fast\-growing organization focused on innovation and operational excellence.
- Fully remote opportunity with global exposure.
Working Hours
This role operates within EST working hours and requires consistent overlap with the U.S.\-based leadership team.
Compensation
Compensation is competitive and dependent on experience, technical background, and overall fit for the role.
Hiring Process
Our hiring process is designed to identify candidates who can combine technical capability, operational thinking, and strong communication skills.
The process includes:
- Resume review and prescreen questionnaire
- One\-way video interview
- Interview with leadership
- Additional/final interview if required
Please note:
Only candidates who complete both the prescreen questionnaire and one\-way video interview will be considered for progression in the hiring process.
All resumes and answers must be submitted in English to be considered for the position.
Requirements:
Preferred Experience
- Experience working within BPOs, contact centers, outsourcing organizations, staffing firms, collections agencies, customer service environments, healthcare support operations, or back\-office operations.
- Experience in identifying and improving business processes.
- Experience participating in client\-facing meetings, operational assessments, or consulting engagements.
- Familiarity with AI tools such as ChatGPT, Claude, Gemini, Copilot, OpenAI APIs, or similar platforms.
- Experience with workflow automation tools such as Zapier, Make, n8n, Power Automate, or similar solutions.
- Experience working with CRM platforms such as Zoho, HubSpot, Salesforce, or Microsoft Dynamics.
- Strong understanding of operational KPIs and workforce management concepts.
- Excellent communication and stakeholder management skills.
- Ability to explain technical concepts to non\-technical audiences.
Ideal Candidate Profile
We are looking for someone who can walk into a client meeting, quickly understand operational challenges, identify inefficiencies, and recommend practical AI and automation solutions that create measurable business value.
The ideal candidate combines operational expertise, business consulting skills, process improvement experience, and AI knowledge into a single skill set.
Success Measures
Success in this role will be measured by:
- AI opportunities identified and implemented
- Operational efficiencies achieved
- Cost savings generated
- Client satisfaction and retention
- Internal process improvements delivered
- Revenue generated through AI consulting and solution design
- Successful deployment and adoption of AI initiatives
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 Resolv.Global, 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.
Resolv.Global AI Hiring
Resolv.Global has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $169,035 across 1,817 positions. About 16% 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 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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