Data Engineer

Tampa, Florida, United StatesHybridFull TimePosted 2 months agoVisa sponsorship available

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Role summary

Our client is seeking an experienced Data Engineer with strong AI/ML expertise to modernize and scale their Business Intelligence (BI) capabilities. This role involves designing and building data pipelines, deploying machine learning solutions, and operationalizing intelligent analytics. The ideal candidate will have a blend of data engineering best practices, applied machine learning, MLOps, and AI experience. Key responsibilities include advanced SQL optimization, Kafka streaming, Databricks workflow development, data governance, performance tuning, and integrating with various data stores. The role also requires building and maintaining ML pipelines, designing data architectures for ML workloads, and exploring generative AI solutions. Vendor management and technical mentoring are also part of the scope.

Data Engineer AI/ML

Hybrid in Tampa, FL, US

Work Authorization : USC/GC

Job Type : Full - Time (Strictly W2 Only - No C2C allowed)

One of our client seeking a Data Engineer with strong AI/ML expertise to modernize and scale our Business Intelligence (BI) capabilities. This role will design and build data pipelines, deploy machine learning solutions, and operationalize intelligent analytics to drive decision-making across the organization. The ideal candidate blends data engineering best practices with applied machine learning, MLOps, and AI.

Key Responsibilities

Project Responsibility: End-to-end data pipelines and integrations

Technical Competencies:

Advanced SQL optimization and complex query design

Kafka streaming applications and connector development

Databricks workflow development with medallion architecture

Data governance implementation and compliance

Performance tuning for large-scale data processing

Data security and privacy best practices

Apache NiFi pipeline development for invoice and PO processing

Integration with purpose-built data stores (Druid, MongoDB, OpenSearch, Postgres)

Build and maintain end-to-end ML pipelines for training, deployment, and monitoring of models.

Design and optimize data architectures for large-scale ML workloads

Explore and implement LLM-based solutions, RAG architectures, and generative AI for business use cases.

Soft Skills:

Cross-functional collaboration with product and engineering teams

Technical mentoring for junior data engineers

Analytical thinking for complex data problems

Stakeholder communication for data requirements

Process improvement and efficiency focus

Quality mindset for data accuracy and reliability Vendor Management:

Direct communication with data platform vendors

Evaluates vendor tools for specific data use cases

Provides technical feedback on vendor product roadmaps

Coordinates with vendors for data integration projects

Qualifications:

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

5+ years in data engineering; 2+ years applying ML in production.

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