Machine Learning Engineer

Designation: -
ML / MLOPs Engineer

Location: -
Noida (Sector- 132)

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.

•
3+ years
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