We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorgan Chase within the Cybersecurity Technology and Controls team, you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
We are seeking a highly skilled Lead Software Engineer with expertise in deploying, monitoring, and managing machine learning models in production environments. This role involves working with cutting-edge technologies to ensure scalable, reliable, and efficient AI solutions. The ideal candidate will be adept at building robust infrastructure and processes to support the seamless operation of machine learning models. In this role, you will be responsible for automating model deployment, optimizing infrastructure, and ensuring the continuous performance of AI systems. Your ability to collaborate with cross-functional teams and address operational challenges will be crucial to driving innovation and delivering impactful AI solutions.
Job responsibilities
Required qualifications, capabilities, and skills
- Obtain 6+ years of applied experience and/or certification in cybersecurity/engineering concepts, Bachelor's degree in Computer Science, Engineering, or a related field, with relevant experience in ML Ops or related roles.
- Advanced Python Programming Skills including Pandas, Numpy and Scikit- Learn
- Proficiency in building and maintaining CI/CD pipelines for machine learning workflows.
- Proficient in all aspects of the Software Development Life Cycle
- Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
- Expertise in cloud platforms (., AWS, Google Cloud, Azure) and containerization technologies (., Docker, Kubernetes).
- Familiarity with monitoring and logging tools (., Prometheus, Grafana, ELK Stack).
- Excellent problem-solving skills and attention to detail and Strong communication skills to collaborate effectively with cross-functional teams.
Hands-on practical experience delivering system design, application development, testing, and operational stability
Preferred qualifications, capabilities, and skills
- Proven experience in deploying and managing large-scale machine learning models in production environments.
- Demonstrated proficiency in software applications and technical processes within a technical discipline (., cloud, artificial intelligence, machine learning, mobile,
- Ability to monitor ML models in production, addressing model performance and data quality issues effectively.
- Working knowledge of security best practices and compliance standards for Machine Learning systems.
- Experience with infrastructure optimization techniques to enhance performance and efficiency.
- Development of REST APIs using frameworks such as Flask or FastAPI for seamless integration into business solutions.
- Familiarity with creating and utilizing synthetic datasets to improve model training and evaluation.
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