TalentAQ

TalentAQ

Machine Learning Engineer

EngineeringFull Time8+ years

Required Skills
39 skills

Machine Learning
Deep Learning
PyTorch
TensorFlow
Scikit-learn
NLP
Computer Vision
Time Series Forecasting
Model Deployment
MLOps
MLflow
Airflow
Docker
Kubernetes
Experiment tracking
CI/CD
ETL
ELT
Apache Spark
dbt
Data warehousing
Redshift
BigQuery
Snowflake
Streaming data
Kafka
Spark Streaming
SQL
Python
Scala
Data architecture
AWS
S3
EC2
SageMaker
Glue
GCP
Vertex AI
Azure

Job Description

<h3>Job Overview</h3><p>We are seeking a talented Machine Learning Engineer to join our growing team. In this role, you will be responsible for developing and deploying machine learning models to solve real-world problems. You will work closely with data scientists and other engineers to build and maintain our AI infrastructure.</p><h3>Key Responsibilities</h3><ul><li>Develop and implement machine learning models using PyTorch, TensorFlow, and Scikit-learn.</li><li>Deploy and monitor machine learning models using MLOps tools.</li><li>Build and optimize ETL/ELT pipelines for machine learning data.</li><li>Collaborate with data scientists to productionize machine learning models.</li><li>Stay up-to-date with the latest advancements in machine learning.</li></ul><h3>Required Skills</h3><ul><li>Proficiency in Machine Learning, Deep Learning, and NLP.</li><li>Experience with cloud platforms such as AWS, GCP, or Azure.</li><li>Strong skills in Python and SQL.</li><li>Experience with MLOps tools such as MLflow, Airflow, Docker, and Kubernetes.</li></ul>

Job Overview

We are seeking a talented Machine Learning Engineer to join our growing team. In this role, you will be responsible for developing and deploying machine learning models to solve real-world problems. You will work closely with data scientists and other engineers to build and maintain our AI infrastructure.

Key Responsibilities

  • Develop and implement machine learning models using PyTorch, TensorFlow, and Scikit-learn.
  • Deploy and monitor machine learning models using MLOps tools.
  • Build and optimize ETL/ELT pipelines for machine learning data.
  • Collaborate with data scientists to productionize machine learning models.
  • Stay up-to-date with the latest advancements in machine learning.

Required Skills

  • Proficiency in Machine Learning, Deep Learning, and NLP.
  • Experience with cloud platforms such as AWS, GCP, or Azure.
  • Strong skills in Python and SQL.
  • Experience with MLOps tools such as MLflow, Airflow, Docker, and Kubernetes.

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