Principal Data & Machine Learning Engineer

AKUVO LLC•Malvern, PA

6d•Hybrid

About The Position

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.

Requirements

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.

Nice To Haves

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).

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.