Machine Learning Engineer

Designation:
ML / MLOPs Engineer

Location:
Noida (Sector- 132)

Experience required:
1 Year

Key Responsibilities:

•
Model Development & Algorithm Optimization:
Design, implement, and optimize
ML models and algorithms
using libraries and frameworks such as
TensorFlow, PyTorch,
and
scikit-learn
to solve complex business problems.

•
Training & Evaluation:
Train and evaluate models using historical data, ensuring accuracy, scalability, and efficiency while fine-tuning hyperparameters.

•
Data Preprocessing & Cleaning:
Clean, preprocess, and transform raw data into a suitable format for model training and evaluation, applying industry best practices to ensure data quality.

•
Feature Engineering:
Conduct feature engineering to extract meaningful features from data that enhance model performance and improve predictive capabilities.

•
Model Deployment & Pipelines:
Build end-to-end pipelines and workflows for deploying machine learning models into production environments, leveraging
Azure Machine Learning
and containerization technologies like
Docker and Kubernetes
.

•
Production Deployment:
Develop and deploy machine learning models to production environments, ensuring scalability and reliability using tools such as
Azure Kubernetes Service (AKS).

•
End-to-End ML Lifecycle Automation:
Automate the end-to-end machine learning lifecycle, including data ingestion, model training, deployment, and monitoring, ensuring seamless operations and faster model iteration.

•
Performance Optimization:
Monitor and improve inference speed and
latency to meet real-time processing requirements
, ensuring efficient and scalable solutions.

•
NLP, CV, GenAI Programming:
Work on machine learning projects involving
Natural Language Processing (NLP), Computer Vision (CV),
and
Generative AI (GenAI)
, applying state-of-the-art techniques and frameworks to improve model performance.

•
Collaboration & CI/CD Integration:
Collaborate with data scientists and engineers to integrate ML models into production workflows, building and maintaining continuous integration/continuous deployment (CI/CD) pipelines using tools like Azure DevOps, Git, and Jenkins.

•
Monitoring & Optimization:
Continuously monitor the performance of deployed models, adjusting parameters and optimizing algorithms to improve accuracy and efficiency.

•
Security & Compliance:
Ensure all machine learning models and processes adhere to industry
security standards and compliance protocols,
such as
GDPR and HIPAA.

•
Documentation & Reporting:
Document machine learning processes, models, and results to ensure reproducibility and effective communication with stakeholders.

Required Qualifications:

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.

•
1 year
of experience in machine learning operations (MLOps), cloud engineering, or similar roles.

• Proficiency in
Python
, with hands-on experience using libraries such as
TensorFlow, PyTorch, scikit-learn, Pandas, and NumPy.

• Strong experience with
Azure Machine Learning
services, including
Azure ML Studio, Azure Databricks,
and
Azure Kubernetes Service (AKS).

• Knowledge and experience in building end-to-end ML pipelines, deploying models, and automating the machine learning lifecycle.

• Expertise in
Docker, Kubernetes, and container orchestration
for deploying machine learning models at scale.

• Experience in
data engineering
practices and familiarity with cloud storage solutions like
Azure Blob Storage
and
Azure Data Lake.

• Strong understanding of
NLP, CV, or GenAI
programming, along with the ability to apply these techniques to real-world business problems.

• Experience with
Git, Azure DevOps,
or similar tools to manage version control and CI/CD pipelines.

• Solid experience in
machine learning algorithms, model training, evaluation,
and
hyperparameter tuning