Machine Learning Engineer

AppTestify

5 years

0 Lacs

pune maharashtra india

Posted:1 day ago| Platform:

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Skills Required

learning design ml ai data preprocessing model training tuning evaluation deployment monitoring efficiency engineering analysis algorithms processing aws azure gcp vertex tracking versioning drift scalability openai automation programming management communication orchestration docker cutting collaborative compensation development

Work Mode

On-site

Job Type

Full Time

Job Description

About the Role:

Machine Learning Engineer

Key Responsibilities:

Develop and implement
machine learning models
for structured and unstructured data.
Perform
data preprocessing
, feature engineering, and exploratory data analysis using
Pandas
and
NumPy
.
Design and maintain
end-to-end ML pipelines
for training, validation, deployment, and monitoring.
Apply and fine-tune ML algorithms using
Scikit-learn
,
TensorFlow
, and
PyTorch
.
Utilize
PySpark
for large-scale data processing and distributed ML workloads.
Implement and manage
model deployment
using
AWS SageMaker
,
Azure ML
, or
GCP Vertex AI
.
Use
MLflow
or similar tools for
experiment tracking, versioning, and reproducibility
.
Monitor and optimize models for performance, drift, and scalability in production environments.
Work with
Large Language Models (LLMs)
such as
OpenAI GPT
and
Hugging Face Transformers
for advanced NLP and generative AI use cases.
Collaborate with Data Scientists, Engineers, and Product teams to integrate ML solutions into production systems.
Contribute to
MLOps practices
, ensuring automation and efficiency across the model lifecycle.
Stay up-to-date with emerging trends in ML, AI frameworks, and cloud-based ML solutions.

Required Skills & Qualifications:

Bachelor’s or Master’s degree in
Computer Science, Data Science, AI/ML, or related field
.
4–5 years of hands-on experience in
Machine Learning Engineering
or a similar role.
Strong programming skills in
Python
with proficiency in
Pandas, NumPy
, and
Scikit-learn
.
Expertise in
TensorFlow
,
PyTorch
, and
PySpark
.
Experience building and deploying
end-to-end ML pipelines
.
Strong understanding of
model evaluation techniques, fine-tuning, and optimization
.
Experience with
MLOps tools
such as
MLflow
,
Kubeflow
, or
DVC
.
Familiarity with
OpenAI
,
Hugging Face Transformers
, and
LLM architectures
.
Proficiency with
cloud ML platforms
like
AWS SageMaker
,
Azure ML
, or
GCP Vertex AI
.
Solid understanding of
model lifecycle management, versioning
, and
experiment reproducibility
.
Excellent
analytical thinking, problem-solving
, and
communication skills
.
Proven ability to work effectively in
cross-functional
and
collaborative
environments.

Nice to Have:

Experience with
data versioning tools
(e.g., DVC, Delta Lake).
Familiarity with
containerization
and
orchestration tools
(Docker, Kubernetes).
Exposure to
generative AI
applications and
prompt engineering
.

Why Join Us:

Opportunity to work on cutting-edge
AI/ML and LLM-based projects
.
Collaborative, growth-driven environment.
Access to the latest
AI tools and cloud ML infrastructure
.
Competitive compensation and professional development opportunities.

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