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About This Role
Help Build the Foundation for the Next Generation of Enterprise Data \& AI
We're looking for an experienced cloud data professional to help lead a multi\-year enterprise modernization initiative that will establish a secure, governed, AI\-ready data platform for a major utility organization.
This isn't a traditional consulting engagement.
You won't rotate through short\-term projects or spend your time chasing billable hours.
Instead, you'll become a trusted technical partner working alongside architects, engineers, security teams, and business leaders to build the foundation for enterprise analytics, operational intelligence, and AI.
This role combines hands\-on engineering, platform architecture, technical leadership, and consulting.
About Waterloo Data
Waterloo Data is a boutique consulting firm specializing in modern data platforms, cloud engineering, enterprise integration, application development, and AI.
We help organizations modernize complex technology environments by combining deep technical expertise with practical business judgment. Our consultants work directly with executive leadership and engineering teams to deliver secure, scalable platforms that create long\-term business value.
We believe the future of AI starts with well\-governed, trusted data.
The Opportunity
Our client is embarking on a large\-scale technology transformation centered on Microsoft Azure and Snowflake.
The program will establish a modern enterprise data platform built according to industry best practices for security, governance, automation, and AI enablement. You'll help bridge existing on\-premises infrastructure with Azure services and Snowflake while introducing modern engineering practices that will serve as the foundation for future business units.
The initial phase of the transformation focuses on a wholesale power organization, where you'll help establish engineering standards, development environments, governance processes, and cloud architecture that will be replicated across the enterprise.
You'll work closely with enterprise architects, infrastructure teams, cybersecurity, application developers, and business stakeholders to ensure the platform is secure, scalable, and built for long\-term success.
What You'll Do
Design and Build a Modern Enterprise Data Platform
- Design and implement secure, scalable Snowflake architectures using industry best practices for security, governance, and performance.
- Develop cloud\-native applications and services using Python and FastAPI.
- Design data ingestion, transformation, and orchestration pipelines that integrate on\-premises systems with Azure and Snowflake.
- Build data products that support enterprise reporting, advanced analytics, and AI use cases.
- Leverage Azure services to create secure, AI\-ready cloud architectures.
Establish Engineering Standards
You'll help create the engineering operating model for a modern enterprise platform by:
- Establishing GitHub repositories and branching strategies.
- Designing and implementing CI/CD pipelines.
- Building standardized Development, QA, UAT, and Production environments.
- Implementing role\-based access control (RBAC).
- Defining secure deployment and release processes.
- Promoting Infrastructure as Code and automation wherever practical.
- Developing reusable engineering patterns and platform standards.
Provide Technical Leadership
- Serve as a trusted technical advisor to client leadership.
- Collaborate with enterprise architects, cybersecurity teams, and infrastructure engineers.
- Mentor client developers and engineers.
- Conduct architecture reviews and code reviews.
- Help establish engineering best practices across the organization.
- Translate business objectives into practical technical solutions.
Required QualificationsExperience
- 8\+ years of professional software engineering, data engineering, or cloud platform experience.
- 4\+ years of enterprise technology consulting experience in a senior consulting role
- 4\+ years of recent, hands\-on experience implementing production solutions with Snowflake.
- 2\+ years of recent experience with Informatica Enterprise Data Catalog (EDC) and Cloud Data Governance and Catalog (CDGC).
- Strong Python development experience, including FastAPI.
- Experience building enterprise solutions in Microsoft Azure.
- Experience integrating on\-premises infrastructure with cloud services.
- Experience designing secure cloud architectures.
- Experience implementing GitHub workflows and CI/CD pipelines.
- Experience designing enterprise RBAC models.
- Experience working directly with enterprise clients or large internal stakeholders.
Preferred Qualifications
We're especially interested in candidates with experience in one or more of the following:
- Electric utility or energy industry experience.
- Enterprise IT modernization programs.
- Snowflake platform administration and performance optimization.
- Azure AI Services or Azure OpenAI.
- Infrastructure as Code (Terraform, Bicep, or similar).
- Enterprise integration and API design.
- Data governance and metadata management.
- DataOps and DevSecOps.
- Agile software delivery.
Technical Skills
Experience across many of the following technologies is highly desirable:
Data Platform
- Snowflake
- SQL
- Informatica Enterprise Data Catalog (EDC)
- Informatica Cloud Data Governance and Catalog (CDGC)
Cloud \& AI
- Microsoft Azure
- Azure Storage
- Azure Functions
- Azure Key Vault
- Azure Entra ID
- Azure AI Services
- Azure OpenAI
Engineering
- Python
- FastAPI
- REST APIs
- GitHub
- GitHub Actions
- CI/CD
- RBAC
- Infrastructure as Code
- DevOps
What Success Looks Like
Within your first year, you will have:
- Helped establish a secure, enterprise\-scale Snowflake platform.
- Built the engineering foundation for a modern Azure data ecosystem.
- Implemented governance capabilities using Informatica EDC and CDGC.
- Established GitHub, CI/CD pipelines, RBAC, and standardized development environments.
- Enabled the first business unit to successfully operate on the new platform.
- Created reusable engineering standards that support future enterprise\-wide adoption.
- Earned the trust of client leadership as a key technical advisor.
Why Waterloo Data?
At Waterloo Data, our consultants are trusted advisors—not just implementers.
You'll work on technically challenging, high\-impact enterprise initiatives alongside experienced architects and engineers, with the opportunity to shape long\-term platform strategy and engineering practices. You'll have the autonomy to solve meaningful problems, influence critical technology decisions, and help build AI\-ready data platforms that will support enterprise operations for years to come.
Pay: From $175,000\.00 per year
Benefits:
- 401(k)
- Dental insurance
- Flexible schedule
- Parental leave
- Retirement plan
Work Location: Hybrid remote in Austin, TX 78701
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 Waterloo Data, 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.
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.
Waterloo Data AI Hiring
Waterloo Data has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
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.
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