Principal Engineer Data & AI

US DEFAULT GROUP INC•Boca Raton, FL

14h

About The Position

The Principal Engineer – Data & AI is a senior, hands‑on lead engineer responsible for defining and implementing enterprise‑scale AI, data, and automation architectures and solutions that drive digital transformation across the organization. This role reports to the Director of AI and Automation and supports the technical direction, architecture standards, and delivery of advanced automation, data platforms, and AI‑driven solutions. This is an engineering‑first role, focused on building and operating production‑grade systems—not a research‑only data science position. The role operates in a highly collaborative environment with AI engineers, automation engineers or data scientists, legal domain experts, and business leaders to deliver AI automation, Gen AI, and advanced analytics solutions at scale.

Requirements

Bachelor’s degree in Computer Science, Data Science, Analytics, or related field (Master’s degree is preferred).
7+ years of software, data, AI or platform engineering experience.
5+ years building data engineering, AI automation, or cloud‑native solutions.
Proven experience delivering production AI systems, including ML, MCP servers, GenAI, LLM, IDP, NLP, and RAG‑based architectures.
Strong hands‑on expertise in Python, SQL, React, C#, Java, and other frameworks.
Strong experience defining enterprise data structures, data migration across tools, metadata catalogs, and data governance standards, with hands‑on implementation of multi‑tier (raw, curated, consumption) data lake or warehouse architectures.
Deep experience with most of the Microsoft Azure Services (Data platforms, Docker, Functions, Kubernetes, Azure Containers, Function Apps, ASB, App Services, GitHub, and ML studio).
Strong stakeholder communication and technical leadership skills.

Nice To Haves

Azure Data Factory, ADLS Gen2, Synapse/Fabric, Azure Databricks, Snowflake
SQL, Python, Java, C#, React, Power Automate, Workato
Playwright, Azure ML, Azure OpenAI, Document Intelligence
Docker, Kubernetes, GitHub Actions, CI/CD, semantic models, vector databases
LangChain, Hugging Face, scikit-learn, TensorFlow, PyTorch, Keras, Hugging Face, OpenCV, NLP, MCP, NLTK, Airflow, Spark, Mistral, ML studio, shell scripting, UAMI, Yaml, and advanced libraries and other open-sources.
Event‑driven systems (Service Bus, Container Apps, AKS, ACS, KEDA, Event Grid, etc.)

Responsibilities

Drives and supports architecture decisions for AI, automation, and data platforms.
Define and maintain reference architectures, design standards, and reusable frameworks.
Design automation involving external applications or sites, APIs, and internal applications through scalable microservices.
Lead implementation of robust lakehouse/warehouse supporting analytics, automation, and AI workloads for agentic AI.
Establish patterns for batch and streaming pipelines, event‑driven architectures, and scalable data access.
Design and build robust data pipelines using technologies such as Azure Data Factory, Azure Data Lake, Snowflake, Databricks, SQL, Spark, Python, or other similar technologies.
Implement strong data quality, lineage, observability, governance, and auditability standards.
Deliver curated datasets, semantic models, and data products for analytics and downstream systems.
Lead development of Intelligent Document Processing (IDP), RAG pipelines, GenAI‑driven architectures, and NLP based querying.
Make the enterprise context and data available easy for business consumption and decisions.
Develop and identify meaningful insights through “big data”, assists in the creation of required ETL pipelines and data structures for Azure Data Lake, Databricks or snowflake, and Data Factory.
Design and deploy AI agents, GenAI models, IDP models, and workflow‑driven AI automation.
Implement and manage MLOps and LLMOps pipelines for training, deployment, monitoring, and governance.
Integrate AI/ML solutions into systems using APIs, microservices, queues, MCP, and containers.
Build secure, compliant RAG architectures with vector search and prompt/version management.
Lead the full lifecycle: discovery, architecture, development, testing, deployment, and support.
Ensure adherence to enterprise security, DevOps, compliance, and data governance standards.
Monitor and optimize performance, reliability, and cost of AI/automation platforms.
Collaborate with the department leader and technical lead to drive technical direction and architecture decisions aligning with standard tech stacks.