AIML Developer Intermediate

InfoVision Inc

4 - 7 years

15 - 25 Lacs

hyderabad

Posted:1 day ago| Platform:

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Skills Required

cd continuous integration python analytical scikit-learn ci/cd aiml numpy artificial intelligence distributed computing containerization pandas tensorflow optimization collaboration devops pytorch cloud computing ml communication skills deployment

Work Mode

Work from Office

Job Type

Full Time

Job Description

Intermediate AI/ML Developer

Requirements:

4–7 years of experience
in AI/ML development, deployment, and optimization.
Strong proficiency in
Python, TensorFlow, PyTorch, or Scikit-learn
.
Experience working with
Google Cloud Vertex AI
for model training and deployment.
Hands-on experience in
ML pipeline development and automation
.
Solid understanding of
cloud computing, distributed computing, and containerization (Docker, Kubernetes)
.
Experience with
data processing tools
like Pandas, NumPy, and Apache Spark.
Knowledge of
MLOps best practices
and tools like MLflow, Kubeflow, or TFX.
Strong problem-solving and analytical skills.
Excellent communication and collaboration abilities.

Preferred Skills:

Experience in
Natural Language Processing (NLP), Computer Vision, or Time-Series Forecasting
.
Working knowledge of
Google BigQuery, Apache Beam, or Airflow
.
Familiarity with
DevOps and CI/CD for ML models
.

Roles and Responsibilities

Design, develop, and deploy
machine learning models
for real-world applications.
Work with
Google Cloud Vertex AI
to build, train, and optimize ML models in a cloud environment.
Implement
data preprocessing, feature engineering, and model evaluation techniques
.
Develop and maintain
ML pipelines
using tools like
Vertex AI Pipelines, TensorFlow Extended (TFX), or Kubeflow
.
Optimize and fine-tune ML models for
performance, accuracy, and scalability
.
Collaboratewith
data engineers, software developers, and business teams
to integrate ML models into production systems.
Work with
big data technologies (Spark, BigQuery, etc.)
for handling large-scale datasets.
Implement
MLOps best practices
for model monitoring, retraining, and deployment automation.
Stay up to date with the latest advancements in
machine learning, deep learning, and AI technologies
.

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