
Sr Data/ MLOps Engineer
Há 4 dias
Role:
Sr Data/ MLOps Engineer
Duration:
Long-Term Contract
Location
: Brazil – Remote
We are seeking a highly motivated and experienced
Data Engineer
with deep expertise in the
MLOps discipline
to join our clients Data Engineering team. In this role, you will be responsible for designing and developing scalable, robust data architectures and MLOps solutions, leveraging the latest technologies from
Google Cloud Platform (GCP)
and
AWS
.
You will collaborate closely with cross-functional teams to understand and address data and ML platform requirements while ensuring the efficient and reliable operation of our ML systems, from model development and training to deployment and inference.
Key Responsibilities
- Design, develop, and maintain scalable, high-performance data infrastructure for real-time and batch data collection, storage, and processing.
- Build reliable, reusable services and APIs to enable seamless data ingestion, transformation, and querying.
- Develop software tools, products, and systems for monitoring and managing ML infrastructure and services efficiently.
- Ensure ML systems operate optimally for model development, training, evaluation, and inference.
- Collaborate with senior management, product teams, and other engineers to deliver high-quality data products.
- Create tools to monitor, debug, and analyze data and ML pipelines.
- Design and implement scalable data schemas and models.
- Mentor team members to elevate overall technical expertise.
- Bring innovative ideas and best practices to advance the company's position in the data and ML space.
Minimum Requirements
- 5+ years of proven experience as a
Data Engineer
or
MLOps Engineer
, preferably in midsize to large data-driven companies. - Strong programming skills in
Python
and either
Java
or
Scala
. - Hands-on experience with
cloud platforms
(GCP preferred; AWS or Azure acceptable). - Experience with
BigQuery
or similar MPP databases (e.g., Redshift, Snowflake). - Practical experience with
ML frameworks
(TensorFlow, PyTorch) and ML deployment patterns. - Proficiency with
Docker
and
Kubernetes
for containerization and orchestration. - Proven experience building
data pipelines
and ETL workflows. - Strong command-line skills and proficiency with version control (Git).
- Excellent communication and collaboration skills.
- Ability to work independently and quickly ramp up productivity.
Preferred Requirements
- Knowledge of distributed data processing frameworks (Apache Kafka, Flink, Spark, etc.).
- Experience with
DBT
(Data Build Tool) and
Looker
. - Familiarity with common ML deployment patterns.
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