AI Engineer (ML Ops & Cloud Engineering Focus)
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.
