Lead Software Engineer - Python AWS GenAI
On-site · Jersey City, New Jersey, United States
Job Summary
Lead Software Engineer at JPMorgan Chase within the MLCOE and Corporate Sector leads the design, development, and deployment of production-grade software and end-to-end ML model pipelines. You will engineer data pipelines to ingest and transform large data volumes, feed processing outputs into ML model pipelines, and store results in data lakes and warehouses. Responsibilities include deploying complete systems into production, solving production issues within SLA, and delivering scalable, secure technology products across multiple technical areas with a focus on Deep Learning, Reinforcement Learning, and cutting-edge ML techniques. Required expertise spans Terraform/AWS IaC, AWS services, data lakes/warehouses (S3, Redshift), CI/CD, dashboards (Tableau/Qlik Sense), SQL and Python data manipulation, PySpark/TensorFlow, REST APIs, and large-scale data processing (Kinesis/Firehose/Glue). Preferred familiarity with large language models and related tooling (Langchain, Haystack).
Required Qualifications
- 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
- Familiarity with recent large language model technologies
- Familiarity with engineering systems using large language models
- Familiarity with LLM tools such as Langchain or Haystack
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