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About This Role
THE OPPORTUNITY
AKUVO is seeking a Principal Data \& Machine Learning Engineer to serve as the senior\-most technical owner across AKUVO’s data platform, machine\-learning models, and the services behind AKUVO IQ. This is a breadth role: you are equally at home building production applications and APIs, engineering the data lake and infrastructure, and developing and deploying predictive models — the person the team turns to at any layer.
You will lead the technical execution of the data and analytics strategy, own architecture across data engineering and machine learning, internalize critical systems currently held by external partners, and provide technical leadership and mentorship to the engineering team. The role combines hands\-on engineering across the full stack with technical leadership and direct ownership of production systems.
LOCATION
Local in Malvern/Philadelphia first, widening to surrounding areas such as New Jersey, New York, Delaware, while continuing to expand geographically in a hybrid/remote capacity based on location.
KEY RESPONSIBILITIES
- Lead the technical execution of the data and analytics strategy across data engineering and machine learning, and own the architecture for AKUVO’s data lake, ML platform, model pipelines, and the data services behind AKUVO IQ.
- Work hands\-on across the full stack — application and API development, systems and infrastructure, data pipelines, and predictive\-model development — stepping directly into whichever layer the team needs.
- Build, deploy, and maintain predictive models and scores alongside the Senior Data \& Machine Learning Engineer, contributing directly to model development as well as the platform beneath it.
- Internalize critical data and ML systems currently held by external partners through a structured knowledge\-transfer and documentation process, building internal depth and reducing concentration risk.
- Design scalable, reliable, and secure architectures for structured portfolio data, predictive\-model data, and separately governed PII and AI\-conversation data.
- Own the operational disciplines for pipelines and production models — monitoring, alerting, incident response, versioning, drift detection, and retraining — so systems can be independently deployed, monitored, and enhanced.
- Evolve technical practices for architecture, development, testing, CI/CD, observability, documentation, and data quality, and ensure data is accurate, timely, and traceable with clear lineage and governance.
- Provide technical leadership, mentorship, and development to the engineering team, set technical direction, and coordinate delivery.
- Partner with Applied AI, the Collections domain, Product, Engineering, Architecture \& Innovation, and Compliance to keep data, models, and AI systems integrated, governed, and production\-ready.
- Evaluate technical investments, cost, and resource needs; make pragmatic build\-versus\-buy decisions; and document and prioritize key risks, dependencies, and technical debt.
- Communicate architecture, risks, and priorities clearly to executive and cross\-functional stakeholders, and advance AI\-assisted engineering practices across the team.
SKILLS AND EXPERIENCE
- 10\+ years across software/data engineering and machine learning, with hands\-on delivery spanning application development, systems and infrastructure, data platforms, and production ML models.
- 3\+ years providing technical leadership and developing engineers.
- Full\-stack breadth — able to build applications and APIs, engineer data pipelines and infrastructure, and develop, deploy, and maintain ML models; the person the team relies on at any layer.
- Deep, hands\-on experience with cloud data and ML platforms in production (Azure strongly preferred) — data lakes, layered architectures, pipelines, product\-serving APIs, and model pipelines.
- Strong Python and SQL, and modern engineering practices (ETL/ELT, CI/CD, observability, testing, environment management).
- A track record of internalizing critical systems and knowledge through structured transitions, and of setting and evolving technical practices.
- Ownership of the production model lifecycle — deployment, versioning, monitoring, drift detection, and retraining.
- Proven ability to translate business and product priorities into scalable roadmaps and pragmatic build\-versus\-buy decisions.
- Strong communication with executive, product, and cross\-functional stakeholders, and comfort operating as a hands\-on technical leader.
- Active, sophisticated use of AI within your own engineering and leadership workflow.
PREFERRED QUALIFICATIONS
- Experience spanning both software/platform engineering and applied ML in the same role — a rare full\-stack\-plus\-modeling breadth.
- Microsoft Fabric and OneLake, or experience leading a Synapse\-to\-Fabric migration; Databricks or comparable ML platforms.
- B2B SaaS, fintech, or financial\-services background (2\+ years), ideally with collections, lending, or credit\-scoring exposure.
- Experience standing up or maturing model governance, documentation, and compliance practices.
- Experience with sensitive, PII, or regulated data and separately governed data zones.
Azure DevOps and structured delivery processes (Epics Features Stories* Tasks).
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 AKUVO, 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. Senior-level AI roles across all categories have a median of $230,000.
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.
AKUVO AI Hiring
AKUVO has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Malvern, PA, US.
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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