Forward Deployed Data Engineer - AI & Data (Hybrid, Frisco)

Kyndryl; Frisco (TX); Hybrid; USD 130,000 - 180,000; Full time

Job summary

Kyndryl's Forward Deployed Data Engineer role in Frisco, TX blends hands-on data engineering with enterprise AI delivery. You will partner with customers to design, build, and scale data foundations powering AI, ML, and autonomous systems in a hybrid environment.

The work covers ETL/ELT pipelines, cloud-native architectures across AWS, Azure, GCP, and development of APIs and vector-based retrieval systems, with a strong emphasis on data quality, governance, and measurable business value.

Qualifications

• Strong coding skills in Python and SQL with hands-on data engineering experience.
• Experience designing and maintaining ETL/ELT pipelines in cloud environments.
• Practical knowledge of vector databases and RAG architectures.
• Experience building APIs and integrating enterprise data systems.

Responsibilities

• Build and optimize scalable ETL/ELT pipelines for batch and real-time processing.
• Design cloud-native data architectures across AWS, Azure, and GCP.
• Develop high-performance APIs and enterprise integrations.
• Implement vector databases and Retrieval-Augmented Generation solutions.
• Support AI and ML workloads in Agentic AI contexts.
• Drive data quality, observability, governance, and compliance.
• Collaborate with customers to deploy solutions delivering measurable value.

Skills

Python

SQL

ETL/ELT

RAG

Semantic search

Cloud AWS

Cloud Azure

Cloud GCP

APIs

Data governance

Tools

Airflow

dbt

Kafka

Spark

Pinecone

Milvus

Weaviate

Chroma

Qdrant

Job description

Kyndryl's Forward Deployed Data Engineer role in Frisco, TX blends hands-on data engineering with enterprise AI delivery. You will partner with customers to design, build, and scale data foundations powering AI, ML, and autonomous systems in a hybrid environment.

The work covers ETL/ELT pipelines, cloud-native architectures across AWS, Azure, GCP, and development of APIs and vector-based retrieval systems, with a strong emphasis on data quality, governance, and measurable business value.