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About This Role
Location:
USA, Remote
Department: NA Delivery Management and Technology
Type: Fulltime
### About Astound Digital
At Astound Digital, we are pioneers in the digital landscape, dedicated to transforming how the world interacts with technology, data, and creativity. Our role as trusted advisors in the digital landscape empowers the world’s most innovative brands with frictionless, end\-to\-end customer experiences. We are known for our comprehensive solutions, proven expertise, and collaborative, nimble approach that instills confidence in our clients. We embrace the power of Artificial Intelligence (AI) to drive innovation, efficiency, and better outcomes for our clients and employees. Join us to navigate and lead in the ever\-changing digital world, where your impact will extend beyond the ordinary.
### Job Purpose
As a Senior Consultant specializing in Anthropic AI, you will sit at the intersection of enterprise cloud architecture and generative AI. You will be instrumental in designing and configuring customized Salesforce.com solutions while pioneering the integration of Anthropic’s Claude models to drive critical business transformations.
You will serve as both a technical and functional expert, partnering with global clients to maximize platform utilization through intelligent automation. Your role involves a blend of high\-level thought leadership and hands\-on development, leveraging Claude Code and advanced AI frameworks to improve organizational efficiency and deliver next\-generation digital outcomes.
### Key Responsibilities
- Design and configure customized Salesforce solutions integrating Anthropic’s Claude to automate complex workflows and enhance data\-driven decision\-making.
- Develop custom applications using Apex, Lightning Web Components (LWC), JavaScript, and Claude Code to accelerate development cycles and meet complex requirements.
- Manage the full system release schedule and rollout process for assigned applications and AI\-integrated projects.
- Research evolving Anthropic features to provide gap analyses and recommend features that improve organizational effectiveness.
- Lead the integration of Salesforce with third\-party systems, AppExchange products, and AI orchestration layers such as AWS Bedrock.
- Author and maintain comprehensive system configuration documentation, utilizing Co\-work for collaborative AI\-driven documentation and support.
- Partner with distributed delivery teams as a technical lead to drive best practices in AI prompt engineering, automation, and modular software design.
### Qualifications and Skills
- 3\+ years of total Salesforce experience with a strong focus on technical consulting and solution design.
- 6 months \- 1 year of dedicated experience working with Anthropic/Claude models.
- Completion of coursework in Anthropic Academy and familiarity with Co\-work and Claude Code.
- 2\+ years of hands\-on experience developing with Lightning Web Components (LWC).
- Possession of at least 4 Salesforce certifications, demonstrating deep platform expertise.
- Aspiration to receive Anthropic Architect certification as it becomes available.
- Proficiency in Salesforce development including Apex Classes, Triggers, Web Services, and Aura Components.
- Proven experience using data tools such as Data Loader and DemandTools for complex migration and integration tasks.
- Strong background in lead roles, such as Lead Developer or Technical Lead, within distributed delivery models.
- Solid understanding of software architecture principles, including object\-oriented design and modularity.
- Excellent technical writing skills and the ability to translate complex application logic into clear documentation.
- Nice to have: Exposure to Gemini, OpenAI, and/or AWS Bedrock.
### What we offer in return
- Global Collaboration: The opportunity to work daily with diverse and talented professionals across the globe.
- Off\-the\-Charts Career Growth: Сlear career path and a performance review system, career coaching, training and certifications, mentoring and knowledge sharing.
- Well\-being Is Top Priority: Parental leave, paid time off, comprehensive health and medical plans.
- Real Work\-Life Balance: Dependent on location, we offer remote, in\-office, or hybrid working modes; flexible hours; work\-life balance support on every stage and level.
### Why work for Astound Digital?
Whether you’re working directly with our world\-renowned clients or with your Astound colleagues from around the globe, you will shape the future of digital commerce, using emerging technologies and innovative approaches.
Grow your career with Astound Digital, and discover exciting opportunities while doing the work you love!
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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Astound Digital, 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 $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Astound Digital AI Hiring
Astound Digital 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 $156,000 across 1,221 positions. About 7% 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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