ML Architect

AAGATIServe

10 years

0 Lacs

hyderabad telangana india

Posted:1 day ago| Platform:

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Skills Required

ml learning design cutting architecture data model development deployment monitoring leadership training governance versioning tuning scalability optimization ai programming tensorflow pytorch aws azure sagemaker vertex containerization orchestration integration spark hadoop communication reinforcement research

Work Mode

On-site

Job Type

Full Time

Job Description

About the Role:

Machine Learning Engineer Architect

Responsibilities

Design and lead the architecture of scalable and reliable ML systems and pipelines.
Collaborate with data engineers to implement ML models in production environments.
Evaluate and select appropriate ML tools, frameworks, and technologies to meet business requirements.
Oversee the full ML lifecycle including data preparation, model development, training, validation, deployment, and monitoring.
Ensure adherence to MLOps best practices and governance (CI/CD for ML, versioning, reproducibility).
Define and implement architecture standards, patterns, and frameworks for machine learning systems.
Lead performance tuning, scalability, and optimization efforts.
Stay up to date with the latest advancements in AI/ML technologies and propose innovative solutions.
Collaborate with stakeholders to translate business needs into ML solutions.

Required Skills & Qualifications:

Bachelor's or Master’s degree in Computer Science, Data Science, AI/ML, or related field.
10+ years of professional experience with at least 5 years in machine learning and data science.
Strong programming skills in
Python
, with experience in ML libraries like
TensorFlow, PyTorch, Scikit-learn, XGBoost
, etc.
Deep understanding of
ML architecture patterns
,
data pipelines
, and
distributed systems
.
Experience with cloud platforms like
AWS, Azure, or GCP
and cloud-native ML tools (SageMaker, Vertex AI, Azure ML).
Proficiency in
Docker
,
Kubernetes
, and other containerization/orchestration tools.
Strong grasp of
MLOps
,
model monitoring
, and
continuous integration/deployment
pipelines.
Hands-on experience with big data technologies like
Spark, Hadoop, Hive
, or similar.
Excellent communication, leadership, and problem-solving skills.

Preferred Qualifications:

Experience with
deep learning
,
NLP
,
computer vision
, or
reinforcement learning
.
Familiarity with
data privacy
,
AI ethics
, and
governance
frameworks.
Contributions to open-source ML projects or research publications.

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