Machine Learning Engineer

Key Responsibilities
:

• Design, implement, and deploy machine learning models to solve complex business problems.
• Develop scalable and efficient machine learning algorithms and systems.
• Analyze large datasets to uncover insights and inform model development.
• Optimize models for performance, accuracy, and scalability.
• Collaborate with data engineers, software engineers, and business stakeholders to integrate machine learning models into production systems.
• Stay current with the latest advancements in machine learning, deep learning, and AI.
• Contribute to code reviews and improve the quality of engineering standards and practices within the team.
• Conduct experiments, evaluate performance metrics, and iteratively improve model performance.
• Provide mentorship and guidance to junior team members and share expertise in machine learning best practices.

Required Skills and Qualifications
:

- 6+ years
of professional experience in machine learning engineering, data science, or a related field.
- Strong proficiency in
Python
,
R
, or
Java
for implementing machine learning algorithms.
- Hands-on experience with popular ML frameworks like
TensorFlow
,
PyTorch
,
Keras
, or
Scikit-learn
.
- Expertise in data preprocessing, feature engineering, and model evaluation techniques.
- Strong understanding of machine learning algorithms, including supervised and unsupervised learning, deep learning, reinforcement learning, etc.
- Experience working with large datasets and distributed computing frameworks (e.g.,
Hadoop
,
Spark
).
- Familiarity with cloud platforms (e.g.,
AWS
,
Azure
,
Google Cloud
) and containerization tools (e.g.,
Docker
,
Kubernetes
).
- Solid understanding of software engineering principles and ability to write efficient, reusable, and modular code.
- Excellent communication skills, both written and verbal, to convey technical concepts to non-technical stakeholders.

Preferred Qualifications
:

- Master's
or
Ph.D.
in Computer Science, Data Science, Engineering, or a related field.
- Experience with
MLOps
and deploying machine learning models to production.
- Familiarity with
SQL
and relational databases.
- Strong knowledge of
cloud-based machine learning platforms
(e.g., AWS SageMaker, Google AI Platform, Azure ML).
- Experience working in Agile environments and using tools like
JIRA
,
Git
, and
CI/CD
pipelines.