We are seeking a highly skilled
AI Engineer
with strong expertise in
machine learning operations (ML Ops), cloud engineering, and large-scale data systems
to support enterprise AI initiatives. This role is
engineering-focused , emphasizing
infrastructure, automation, and integration
rather than model development. The ideal candidate has experience with
end-to-end ML model lifecycle management ,
scalable system architecture , and
enterprise AI applications
in a cloud environment.
Key Responsibilities:
ML Ops & Infrastructure:
Design, implement, and maintain
scalable, cloud-based ML pipelines
using
AWS (SageMaker, Unified Studio, Mayflower or equivalents)
for
model deployment, monitoring, and automation .
Big Data & Cloud Engineering:
Build and optimize
high-performance data pipelines and distributed computing solutions
for processing
large-scale datasets .
Enterprise AI Systems:
Develop and integrate AI-driven solutions into
enterprise-grade financial applications (CCFA apps) , ensuring compliance with
common standards, security, and best practices .
Software Development:
Write production-ready code in
Python, C++, and other relevant languages
to support AI system implementation and infrastructure scaling.
Database & Performance Optimization:
Work with
SQL, NoSQL, and large-scale database systems
to ensure efficient data retrieval, transformation, and storage for AI applications.
Collaboration & Architecture:
Partner with
data engineers, cloud architects, and quant engineers
to develop and maintain
robust AI-driven workflows
in a cloud-based, enterprise environment.
Model Lineage & Governance:
Implement
ML model lifecycle tracking, data lineage, and governance frameworks
to ensure AI system transparency and compliance.
Qualifications:
5+ years of experience
in software engineering, cloud infrastructure, or ML Ops.
Strong programming skills in
Python, C++, SQL , and experience with
cloud-based AI services (AWS SageMaker, Mayflower, Unified Studio, etc.) .
Deep understanding of
ML Ops, model lifecycle management, and AI deployment strategies .
Experience working with
large-scale, enterprise applications , preferably in
financial services .
Familiarity with
big data processing frameworks (Spark, Kafka, or similar)
and
cloud-based AI/ML pipelines .
Strong problem-solving skills and ability to work with
cross-functional teams
in a fast-paced environment.
This role is ideal for an
experienced engineer who understands AI/ML workflows but focuses on infrastructure, deployment, and scaling rather than developing new ML models . If you are passionate about
AI-driven engineering, cloud automation, and enterprise-grade AI solutions , we encourage you to apply.
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