AI/ML Data Engineer (Fulltime)

Aptonet; Tampa (FL); Hybrid; USD 100,000 - 130,000; Full time

Benefits offered by this job

Employer-matched 401(k)

Company-paid medical insurance

Company-paid vision insurance

Company-paid life and AD&D insurance

18 paid vacation days annually

Holiday bonus

Incentive compensation

Employee recognition programs

Community outreach opportunities

Business casual dress code

Job summary

A leading tech company is seeking a highly skilled Data Engineer with strong AI/ML experience to design and build data pipelines and deploy machine learning solutions. The ideal candidate will have at least 5 years in data engineering and 2 years deploying ML in production. This hybrid role offers employer-matched 401(k), paid medical insurance, and 18 paid vacation days annually, amongst other benefits.

Qualifications

• 5+ years of experience in data engineering.
• 2+ years of experience deploying machine learning solutions in production environments.
• Strong experience with distributed data systems and modern data platforms.

Responsibilities

• Design, build, and maintain end-to-end data pipelines and integrations.
• Develop and optimize advanced SQL queries for high-performance analytics.
• Implement MLOps best practices for model lifecycle management.

Skills

Data engineering fundamentals

Applied machine learning expertise

MLOps best practices

SQL

Kafka

Apache NiFi

Data governance standards

ML pipeline development

Education

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Statistics, or related field

Tools

Databricks

Druid

MongoDB

OpenSearch

Postgres

Job description

Location: Hybrid – Tampa, FL (2 days onsite)

We are seeking a highly skilled Data Engineer with strong AI/ML experience to help modernize and scale enterprise Business Intelligence and advanced analytics capabilities. This role will design and build robust data pipelines, deploy production‑ready machine learning solutions, and operationalize intelligent analytics to support data‑driven decision‑making across the organization.

The ideal candidate combines strong data engineering fundamentals with applied machine learning expertise, MLOps best practices, and experience working with modern AI technologies.

Key Responsibilities

Data Engineering & Architecture

• Design, build, and maintain end‑to‑end data pipelines and integrations
• Develop and optimize advanced SQL queries for high‑performance analytics
• Build Kafka streaming applications and connectors
• Develop Databricks workflows leveraging medallion architecture
• Implement data governance standards, compliance controls, and security best practices
• Develop Apache NiFi pipelines, including invoice and PO processing workflows
• Integrate with purpose‑built data stores including Druid, MongoDB, OpenSearch, and Postgres
• Build and maintain end‑to‑end ML pipelines for model training, deployment, monitoring, and optimization
• Design scalable data architectures to support large ML workloads
• Implement MLOps best practices for model lifecycle management
• Explore and deploy LLM‑based solutions, Retrieval‑Augmented Generation (RAG) architectures, and generative AI use cases
• Partner cross‑functionally with product, engineering, and business stakeholders
• Translate business requirements into scalable data solutions
• Mentor junior data engineers and promote engineering best practices
• Communicate complex technical concepts clearly to stakeholders
• Drive process improvements and ensure high standards of data accuracy and reliability
• Serve as a technical liaison with data platform vendors
• Evaluate vendor tools for targeted data and AI use cases
• Provide feedback on product roadmaps

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Statistics, or related field
• 5+ years of experience in data engineering
• 2+ years of experience deploying machine learning solutions in production environments
• Strong experience with distributed data systems and modern data platforms
• Experience working with AI/ML frameworks and MLOps tooling
• Hybrid work environment
• Employer‑matched 401(k)
• Company‑paid medical insurance option (employee + dependent children)
• Company‑paid vision insurance (employee)
• Company‑paid long‑term and short‑term disability
• Company‑paid life and AD&D insurance
• 18 paid vacation days annually
• Six paid holidays
• Holiday bonus
• Incentive compensation
• Employee recognition programs
• Community outreach opportunities
• Business casual dress code