Platform Engineer - (AI/ML) Financial Services

Full Description

This role will focus on development and operation of AI/Machine Learning platform for our client, as key player with hands-on skills in Python.

Job Responsibilities:

• Develop and Enhance AI/ML Platforms: Contribute to the development, monitoring, logging, and deployment of proprietary, open-source, and in-house applications.

• Service Development & API Integration: Build and integrate services with both internal and external APIs.

• Technology Evaluation & Optimization: Assess and implement new technologies to enhance infrastructure performance, scalability, and maintainability.

• Platform Monitoring & Performance Insights: Ensure transparency into the structure, health, and efficiency of the AI/ML platform.

• Collaborate on Software Delivery: Work closely with Architects, Platform Engineers, and Product Owners to develop and deploy software in a continuous integration and delivery (CI/CD) environment.

• Support Data Science & Business Teams: Partner with data scientists and business stakeholders to facilitate the development and deployment of analytics and machine learning models.

Requirements:

• Educational Background: Bachelor’s degree in Computer Science, Software Engineering, or a related field.

• Experience: Minimum of 3 years in AI/ML platform development.

• Technical Expertise: Strong foundation in software engineering and system design.

• Proficiency in Python.

• Preferred Experience:

• Working with DataOps/MLOps in large-scale enterprise environments.

• Managing large-scale data infrastructure (handling hundreds of terabytes).

• Adopting public cloud platforms such as AWS, Azure, or AliCloud.

• Experience working in international teams (financial services background is a plus).

• Strong communication skills and a data-driven approach to problem-solving.

• Proven ability to manage teams, drive initiatives, and solve complex challenges.

• Self-motivated, creative, and capable of working independently while collaborating effectively in a team environment.

• Technical Stack Proficiency:

• Infrastructure: Docker, Terraform, Kubernetes

• AI/ML Platforms: Jupyter, MLflow, Ray

• Data Engineering: Airflow, Spark

• CI/CD Tools: Jenkins, JIRA, Sonar, Git, Ansible

• Testing Frameworks: jMeter, jUnit, PyTest