Lead Software Engineer - Platform Engineering at JPMC Candidate Experience page

Jersey City, New Jersey, United States -

Full Time

Start Date

Immediate

Expiry Date

12 Jun, 26

Salary

215000.0

Posted On

14 Mar, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Software Engineering, Machine Learning, Deep Learning, Reinforcement Learning, Terraform, AWS Infrastructure As Code, AWS CloudWatch, Amazon S3, AWS Redshift, AWS Services, CI/CD Pipelines, Tableau, Qlik Sense, SQL, Python, PySpark

Industry

Financial Services

Description

Join JPMorganChase’s MLCOE to turn cutting-edge AI into production at enterprise scale. The Machine Learning Center of Excellence (MLCOE) team partners across the firm to create and share Machine Learning Solutions for our most challenging business problems. In this role you will work and collaborate with a team comprised of a multi-disciplinary community of experts focused exclusively on Machine Learning. On this team you will work with cutting-edge techniques in disciplines such as Deep Learning and Reinforcement Learning. As a Lead Software Engineer at JPMorgan Chase within the Corporate Sector – AIML Data Platforms and Machine Learning Center of Excellence Team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives. Job responsibilities * Designs, develops and maintains production grade software * Engineers data pipelines to ingest and transform large volumes of data. * Feeds processed data into machine learning model pipelines. * Stores resulting data in data warehouses and data lakes. * Design sand implements end-to-end machine learning model pipelines, from data input to serving outputs to a large user base. * Deploys complete systems into production environments. * Resolves production issues within a 5-hour SLA. * Adds new features and maintains product code to ensure ongoing system performance and reliability. Required qualifications, capabilities, and skills * Formal training or certification on software engineering concepts and 5+ years applied experience * Infrastructure design for large-scale machine learning model deployment using tools like Terraform or AWS Infrastructure as Code. * Building metrics and setting up AWS CloudWatch monitors and alarms for infrastructure and application performance. * Working with data lakes (Amazon S3) and data warehouses (AWS Redshift). * Utilizing AWS services and CI/CD pipelines for deploying and maintaining machine learning applications. * Developing dynamic, interactive dashboards with Tableau or Qlik Sense, including advanced visualization, ETL automation, and ODBC connectors. * Data manipulation, structuring, design flow, and query optimization using SQL and Python. * Processing large datasets with data containers, multithreading, and multiprocessing in PySpark and TensorFlow. * Developing software or microservices deployed as REST APIs. * Using AWS Kinesis and Firehose for large-scale data ingestion and ETL with AWS Glue. * Developing and automating high-performance, large-scale data processing systems. Preferred qualifications, capabilities, and skills * Familiarity with recent large language model technologies * Familiarity with engineering systems using large language models * Familiarity with LLM tools such as Langchain or Haystack JPMorganChase, one of the oldest financial institutions, offers innovative financial solutions to millions of consumers, small businesses and many of the world’s most prominent corporate, institutional and government clients under the J.P. Morgan and Chase brands. Our history spans over 200 years and today we are a leader in investment banking, consumer and small business banking, commercial banking, financial transaction processing and asset management. We offer a competitive total rewards package including base salary determined based on the role, experience, skill set and location. Those in eligible roles may receive commission-based pay and/or discretionary incentive compensation, paid in the form of cash and/or forfeitable equity, awarded in recognition of individual achievements and contributions. We also offer a range of benefits and programs to meet employee needs, based on eligibility. These benefits include comprehensive health care coverage, on-site health and wellness centers, a retirement savings plan, backup childcare, tuition reimbursement, mental health support, financial coaching and more. Additional details about total compensation and benefits will be provided during the hiring process. We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs [https://careers.jpmorgan.com/us/en/how-we-hire/faqs] for more information about requesting an accommodation. JPMorgan Chase & Co. is an Equal Opportunity Employer, including Disability/Veterans Our professionals in our Corporate Functions cover a diverse range of areas from finance and risk to human resources and marketing. Our corporate teams are an essential part of our company, ensuring that we’re setting our businesses, clients, customers and employees up for success.

Responsibilities

The Lead Software Engineer will design, develop, and maintain production-grade software, focusing on engineering data pipelines to ingest and transform large data volumes for machine learning model pipelines. Responsibilities also include designing end-to-end ML pipelines, deploying complete systems into production, and resolving production issues within a 5-hour SLA.