Machine Learning Engineer

JobID: 210516469

Category: Predictive Science

JobSchedule: Full time

Posted Date: 2024-05-13T16:51:22+00:00

JobShift:

Base Pay/Salary: Jersey City,NJ $147,250.00-$260,000.00

Are you looking for an exciting opportunity to join a dynamic and growing team in a fast paced and challenging area? This is a unique opportunity apply your skills and have a direct impact on global business. You will be building and training production-grade ML models on large-scale datasets, developing end-to-end ML pipelines, and collaborating to develop large-scale data modeling experiments. Your expertise in Python, PySpark, DL frameworks like TensorFlow, and MLOps will be crucial in this role.

As an experienced Machine Learning Engineer, you will work as part of the Storage team within Compute Network Storage(CNS), working with an inspiring and curious team of technologists dedicated to deploying and developing large-scale infrastructure solutions that support JPMorgan Chase & Co's diverse and critical businesses. The Compute Network Storage(CNS) group, within Infrastructure Platforms (IP) is responsible for defining, developing and operating cloud products consumed by our Application Development Partners across the firm. Our Product Portfolio includes Private and Public Cloud Platforms and a wide range of services such as databases, messaging and telemetry.

Job Responsibilities

• Design, develop, and implement machine learning algorithms and models to solve specific business problems.

• Collaborate with data scientists and domain experts to identify and engineer relevant features for improving model accuracy and robustness.

• Deploy machine learning models into production systems, ensuring scalability, reliability, and efficiency.

• Work closely with product managers, software engineers, and other stakeholders to understand requirements, prioritize tasks, and deliver ML solutions that meet business objectives.

• Implement monitoring and logging mechanisms to track model performance in real-time and address any issues that arise.

• Conduct hyper parameter optimization to fine-tune model performance and improve generalization on unseen data.

• Research and analyze data sets using a variety of statistical and machine learning techniques

• Communicate AI capabilities and results to both technical and non-technical audiences

• Document approaches taken, techniques used and processes followed to comply with industry regulation

• Collaborate closely with cloud and SRE teams while taking a leading role in the design and delivery of the production architectures for our solutions.

Required Qualifications, Capabilities, And Skills

• Proficiency in programming languages such as Python, Java

• Strong understanding of machine learning algorithms, including time series algorithms, deep learning, reinforcement learning, and classical ML techniques.

• Experience with machine learning frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, etc.

• Solid understanding of software engineering principles, including design patterns, data structures, and algorithms.

• Track record of developing, deploying business critical machine learning models

• Good exposure to ML ecosystem components like Feature Store, Feature Registry and MLOps

• Broad knowledge of MLOps tooling - for versioning, reproducibility, observability etc

• Experience monitoring, maintaining, enhancing existing models over an extended time period

• Solid understanding of fundamentals of statistics, optimization and ML theory. Familiarity with popular deep learning architectures (transformers, CNN, auto encoders etc.)

• Hands-on experience in implementing distributed/multi-threaded/scalable applications (incl. frameworks such as Apache Spark, Dask etc.)

• Able to communicate technical information and ideas at all levels; convey information clearly and create trust with stakeholders.

Preferred Qualifications, Capabilities, And Skills

• Experience with cloud computing platforms such as AWS, Azure, or Google Cloud Platform.

• Experience of big data technologies (e.g. Spark, Hadoop, Apache Kafka)

• Knowledge of containerization technologies such as Docker and Kubernetes.

• Have constructed batch and streaming micro services exposed as REST/gRPC endpoint.

• Familiarity with version control systems such as Git and collaboration tools like Jira.

• Experience with continuous integration and continuous deployment (CI/CD) pipelines.