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About This Role
Overview:
At JDA TSG, we equip many of the world’s major brands with top\-tier specialized talent, business process expertise and technologies to drive their organizations in exciting new directions. What makes us the partner of choice for the most experience\-obsessed brands worldwide? We apply extensive due diligence up front to ensure that our teams and talent will be a cultural fit and can make a difference from the very start. And we’ve established a reputation for bringing exceptional focus, flexibility, and confidence with every client we serve.
We’re seeking a Azure Data \& AI Strategy Expert who can guide enterprise customers through modern data estate modernization and AI enablement using Microsoft’s Azure data platform. This role focuses on aligning Purview (Data), Microsoft Fabric, Databricks, and Azure AI Foundry to build secure, governed, and AI\-ready data environments.
In this customer\-facing role, you’ll help organizations unlock business value by advising on data lineage,
governance best practices, real\-time analytics, and scalable AI infrastructure — all while ensuring trust, performance, and compliance.
What You Will Do:
- Guide enterprise customers in designing governed data foundations using Azure Purview, Microsoft Fabric, and Databricks.
- Lead workshops and strategy sessions around data readiness for AI, including architecture reviews, risk assessments, and value roadmaps.
- Help define and align data governance models, including metadata management, lineage, domains, and quality controls.
- Support customer adoption of Microsoft Fabric by advising on lakehouse strategies, real\-time pipelines, and integration with Power BI and Copilot.
- Provide advisory on integrating Databricks with the broader Microsoft data stack and establishing enterprise\-scale ML ops foundations.
- Support the implementation of AI Foundry scenarios, ensuring data quality, governance, and cost optimization across AI solutions.
- Advocate for secure and responsible AI deployment strategies across the data lifecycle.
Who You Are:
Candidate Will Have:
- 10\+ years of IT experience
- 7\+ year of experience in data architecture, governance, or enterprise analytics strategy.
- Familiarity with Microsoft Fabric, Databricks, and the Azure data platform.
- Experience advising on data modernization projects, particularly in AI\-adopting enterprises.
- Solid understanding of data governance principles, business metadata, and operational AI enablement.
- Executive presence with ability to translate complex data infrastructure into business value narratives.
Key Skills \& Technologies:
- Data Governance: Azure Purview (catalog, lineage, quality scans, domains)
- Data Platforms: Microsoft Fabric (OneLake, Data Factory, Data Activator, Real\-Time Hub), Databricks, Azure Synapse
- AI Enablement: Azure AI Foundry, model lifecycle readiness, AI observability
- Architecture: Lakehouse, Delta Lake, Spark, Power BI integration
- Security \& Compliance: Data protection strategy, labeling, role\-based access, audit trails
- Strategy: Cloud Adoption Framework, Responsible AI principles, FinOps\-aware design
What We Offer:
- Healthcare \- Comprehensive coverage for you and your family
- Employee Assistance Program \- Get support when you or your family need it with counseling and coaching
- 401K with company match
- Paid time off
- Paid parental leave
- Volunteer Day Off
- Life insurance \- Protect your loved ones and their future
- Business travel accident insurance
Posted Salary Range: USD $110,000\.00 \- USD $120,000\.00 /Yr.
Salary Context
This $110K-$120K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At JDA TSG, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($115K) sits 37% below the category median. Disclosed range: $110K to $120K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
JDA TSG AI Hiring
JDA TSG has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $110K - $120K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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