ML Engineer, Data & ML Infrastructure

Tekfortune is a fast-growing consulting firm specialized in permanent, contract & project-based staffing services for world s leading organizations in a broad range of industries. In this quickly changing economic landscape, virtual recruiting and remote work are critical for the future of work. To support the active project demands and skills gaps, our staffing experts can help you find the best job for you.


ML Engineer, Data & ML Infrastructure
Contract 12 MONTHS+
REMOTE

Top Skills:
GCP, Kafka, Airflow, Python, Spark, Hive

From hiring manager:
My team builds infrastructure used by inference pipelines in production.
We have a specific project in mind and I need to confirm whether it is required to have knowledge/skills in feature stores specifically.
The role is not to train models. We do support Data Scientists with data during development, but we aren't training the models ourselves.

Role Overview

This role builds and maintains the data infrastructure that powers ML systems in production it is not a model-building or model-training position. The team owns the infrastructure used by inference pipelines in production, and separately supports Data Scientists by providing and preparing data during model development. Model training itself sits outside this team's scope.
In practice, this is best understood as a Senior Data Engineer role focused on feeding ML/inference systems success depends on deep data pipeline and orchestration expertise more than on ML modeling experience.

What You'll Do
" Build and maintain the data pipelines that feed ML inference systems running in production.
" Support Data Scientists by supplying, preparing, and validating data during model development (without owning model training).
" Design and operate data processing jobs on Dataflow and Dataproc (Spark) for large-scale transforms.
" Own data lake structure and organization in Cloud Storage to support downstream ML and inference consumers.
" Build and maintain orchestration workflows (Cloud Composer/Airflow) that reliably schedule and monitor pipeline execution.
" Implement data quality checks and testing frameworks across pipelines.
" Contribute to feature engineering pipelines supporting a specific upcoming project (feature store tooling see note below).

Required Experience
" 7+ years of software engineering experience, with 3+ years specifically in data infrastructure.
" Strong, hands-on expertise with GCP's data and ML infrastructure stack: Dataflow, Cloud Storage, Cloud Composer, Dataproc.
" Deep expertise in Spark for large-scale data processing.
" Proficiency in Python and SQL.
" Experience building data quality and testing frameworks.
" Experience with pipeline orchestration tools (Airflow, Dagster).

Preferred / Project-Dependent
There is a specific upcoming project where feature store experience may be required. Confirm current need before screening candidates against this line item.
" Vertex AI Feature Store experience, or strong transferable experience with Feast or Tecton (feature versioning, online/offline serving parity).
" Experience with data versioning tools.

What This Role Is Not
" Not a model training or model development role.
" Not responsible for model architecture, evaluation, or ML research.





For more information and other jobs available please contact our recruitment team at

careers@tekfortune.com

. To view all the jobs available in the USA and Asia please visit our website at https://www.tekfortune.com/careers/ .