Take on a crucial role where you'll be a key part of a high-performing team delivering secure software solutions. Make a real impact as you help shape the future of software security at one of the world's largest and most influential companies. 

As a Lead Security Engineer at JPMorgan Chase within the Cybersecurity and Technology Controls line of business, you are an integral part of team that works to deliver software solutions that satisfy pre-defined functional and user requirements with the added dimension of preventing misuse, circumvention, and malicious behavior. As a core technical contributor, you are responsible for carrying out critical technology solutions with tamper-proof, audit defensible methods across multiple technical areas within various business functions. 

We are seeking a highly skilled ML Ops 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

Collaborate with cross-functional teams, including data scientists and software engineers, to understand model requirements and integrate them into applications. Develop and implement strategies for deploying machine learning models into production, ensuring scalability, reliability, and efficiency. Design and maintain continuous integration and continuous deployment (CI/CD) pipelines to automate the testing, deployment, and updating of machine learning models. Manage and optimize the infrastructure required for running machine learning models, including cloud services, containerization (e.g., Docker), and orchestration tools (e.g., Kubernetes). Implement monitoring and logging solutions to track model performance, detect anomalies, and ensure models are operating as expected in production. Maintain version control for models and data, ensuring traceability and compliance with governance policies. Respond to incidents and troubleshoot issues related to model performance, data quality, and infrastructure. Executes creative security solutions, design, development, and technical troubleshooting with the ability to think beyond routine or conventional approaches to build solutions and break down technical problems. Develops secure and high-quality production code and reviews and debugs code written by others Minimizes security vulnerabilities by following industry insights and governmental regulations to continuously evolve security protocols, including creating processes to determine the effectiveness of current controls Works with stakeholders and business leaders to understand security needs and recommend business modifications during periods of vulnerability. Adds to team culture of diversity, equity, inclusion, and respect.

Required qualifications, capabilities, and skills

Formal training or certification on security engineering concepts and 5+ years applied experience Skilled in planning, designing, and implementing enterprise level security solutions Advanced in one or more programming languages Proficient in all aspects of the Software Development Life Cycle Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security In-depth knowledge of the financial services industry and their IT systems Strong expertise in deploying and managing machine learning models in production environments. Proficiency in building and maintaining CI/CD pipelines for machine learning workflows. Expertise in cloud platforms (e.g., AWS, Google Cloud, Azure), containerization technologies (e.g., Docker, Kubernetes). Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack). Advanced Python Programming Skills including Pandas, Numpy and Scikit-Learn. Strong SQL skills a plus.

Preferred qualifications, capabilities, and skills

Proven experience in deploying and managing large-scale machine learning models in production environments. Strong 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. Bachelor's degree in Computer Science, Engineering, or a related field, with relevant experience in ML Ops or related roles.