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About This Role
AI Solutions Architect
Assess • Architect • Enable • AI That Moves the Business
About Digacore
At Digacore, we help businesses operate smarter through technology, automation, and exceptional support. We’re a fast\-paced, people\-first MSP that blends IT, development, automation, and client experience into one collaborative environment — with real career growth, autonomy to own your work, and leadership that values innovation and practical solutions over bureaucracy.
We’re looking for an AI Solutions Architect to lead how Digacore and our clients adopt AI and automation — from discovery through delivery. This is an architect and consultant role, not a hands\-on build role: you scope, assess, and design the solution, and our engineers build it. You’ll sit with clients and internal teams to understand where they are today, map where AI and automation can take them, and design the solutions and data pipelines to get there — owning each engagement through delivery.
We’re starting internal\-first. You’ll assess and structure our own data and workflows before — and while — we bring this practice to clients, so we practice what we sell.
What You’ll Do
- Run client AI Readiness Assessments — deep discovery across data pipelines and workflows/processes to find where AI and automation create real value
- Build the roadmap — turn each assessment into a 30/60/90\-day automation \& AI roadmap the client reviews and signs off on
- Architect the solution — design the solution and pipeline structure, hand the build to our engineers, and own it through delivery
- Lead client enablement and training — move client staff along the maturity curve: from treating AI as “a Google search,” to “an assistant,” to “an employee working by their side”
- Drive the internal\-first phase — assess and structure Digacore’s own data and workflows so we’re ready to scale the practice to clients
- Partner and prioritize — work with leadership, department heads, and clients to translate business problems into scoped, high\-impact solutions
- Set direction and standards — define how we assess, architect, and deliver AI and automation work across the company and client environments
- Track and communicate impact — time saved, efficiency gained, and clear business and client value delivered
What You Bring
Required Skills \& Experience
- A relentless AI\-frontier researcher — you live at the edge of new models, tools, and patterns and are always learning
- Business \+ data \+ people translation — you can sit with a non\-technical stakeholder and a data pipeline in the same hour and connect the two
- Genuine conviction about AI — you see it as an opportunity, not a threat, and can bring skeptical clients along
- Comfort with ambiguity — you don’t need a playbook; you create the structure yourself
- A strong “data brain” — you think in terms of workflow, process, and what the end result should actually drive
- An architecture/consulting track record — you’ve scoped and designed data, automation, or AI solutions (design and direction, not primarily coding)
- Excellent communication — comfortable presenting roadmaps and recommendations to executives, leadership, and clients
- Multi\-client environment experience — MSP, SaaS, or another service business where you’ve worked across many clients
Preferred Skills \& Experience
- Familiarity with the modern AI/automation toolchain (Claude, LLM platforms and APIs, n8n, Power Platform) — enough to design with it and direct engineers, not to write production code yourself
- Working knowledge of data structures, pipelines, and reporting tools (e.g., Power BI)
- Familiarity with Microsoft 365, SharePoint, and Azure
- Experience running discovery or readiness assessments and building client\-facing roadmaps
The Right Mindset
We care just as much about mindset as technical ability. The ideal person for this role:
- Is genuinely excited about AI and treats new models, tools, and patterns as opportunities, not threats
- Loves sitting with people and watching them work — that’s where the best assessments start
- Thinks in terms of outcomes and business value, not activity
- Can stay calm and professional when clients are stressed or unsure
- Has a “white glove” approach to client experience
- Thrives in ambiguity and builds structure where there wasn’t any
- Wants to grow into leading our AI practice over time
Growth Path
This role starts as an individual contributor with a clear runway. As the practice scales, there’s a path to lead our AI\-frontier team — owning assessments, deployments, and training — or to manage the Development \& Automation (D\&A) department.
How You’ll Be Measured
- Quality and business impact of the AI readiness assessments and roadmaps you deliver
- Client sign\-off and adoption of the roadmaps you build
- Successful delivery of the solutions you architect — from scope through handoff to engineering
- Growth in client AI maturity — staff moving from “Google search” to “assistant” to “employee by their side”
- Progress structuring and activating Digacore’s own data and workflows
Compensation \& Location
- Compensation: 120\-150k, DOE
- Location: New Jersey hybrid preferred; remote considered for the right person
Salary Context
This $120K-$150K range is in the lower quartile 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 DigaCore, 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 ($135K) sits 38% below the category median. Disclosed range: $120K to $150K.
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.
DigaCore AI Hiring
DigaCore has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Lakewood, NJ, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
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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