Software Engineer [Multiple Positions Available]

JPMorganChase•Plano, TX

2d•Onsite

About The Position

We are seeking a highly skilled Software Engineer to lead and manage a team in designing, developing, and deploying enterprise-scale financial applications and data-driven platforms. This role involves making critical architectural decisions for distributed, cloud-native, and microservices-based systems, ensuring scalability, resiliency, and security. You will provide technical leadership in implementing multi-cloud strategies, driving the adoption of data engineering and orchestration tools, and overseeing the integration of machine learning solutions into production systems. The position also requires implementing MLOps frameworks, service mesh technologies, and search/indexing technologies. Collaboration with cross-functional teams, establishing best practices in Agile SDLC, and managing project budgets, timelines, and risks are key aspects of this role. Mentoring and coaching engineers to foster technical expertise and leadership is also a core responsibility.

Requirements

Bachelor's degree in Computer Science, Information Technology, Computer Engineering, Computer Information Systems, Information Technology, Data Engineering, or related field of study plus 7 years of experience in the job offered or as Software Engineer, Developer, IT Consultant or related occupation.
Leading and managing software engineering teams, including mentoring, hiring and performance evaluation.
Designing and implementing microservice-based application and infrastructure architectures on AWS, including containerized workloads using ECS, Fargate, and Lambda, and service mesh technologies including Istio to ensure scalability, reliability, and operational efficiency.
Developing and maintaining end-to-end data engineering workflows using orchestration tools such as Apache Airflow or Prefect, data processing frameworks including PySpark and Kafka, and cloud-based data services to automate ingestion, transformation, and integration of structured and unstructured datasets.
Python and its libraries and frameworks.
Working with large-scale enterprise data, including financial, operational, and behavioral datasets, used for analytics, reporting, and data-driven decision-making including Pandas, NumPy and PySpark.
Developing and deploying machine learning and AI applications using frameworks including TensorFlow, PyTorch, and scikit-learn, including integrating pre-trained models, performing evaluation and tuning, and managing deployment workflows through platforms such as MLflow or Kubeflow.
Designing and developing data visualizations using tools including Matplotlib, Seaborn, and Plotly to present analytical results, model insights, and key performance metrics to technical and business stakeholders.
Designing and developing RESTful APIs and web services using frameworks including FastAPI, Flask, and Django, including API integration, performance optimization, and secure data access controls.
Designing, implementing, and managing orchestration and ETL pipelines using tools such as Airflow, Prefect, or AWS Step Functions to automate ingestion, transformation, validation, and loading of data into analytical or operational systems.
Utilizing MLOps frameworks such as MLflow or Kubeflow for ML model deployment, monitoring, and governance.
Utilizing service mesh technologies such as Istio or Linkerd for microservices management and observability.
Using search and indexing technologies such as Elasticsearch, OpenSearch, or Algolia for managing and querying large datasets, including schema design, indexing strategies, and query optimization.
Developing software solutions using programming languages and frameworks including Python, Java, Node.js, JavaScript, and C#, emphasizing modular design, scalability, and CI/CD integration using Jenkins, Jules, and Spinnaker.
Designing and managing CI/CD pipelines such as Jenkins, Git, Code Pipeline, or Azure DevOps.
Applying software testing methodologies including unit, integration, regression, performance, and automated testing.
Implementing testing frameworks such as PyTest, JUnit, or Postman.
Integrating test coverage into CI/CD pipelines to ensure reliability and code quality.

Responsibilities

Lead and manage a team of software engineers in designing, developing, and deploying enterprise-scale financial applications and data-driven platforms.
Make architectural decisions across distributed, cloud-native, and microservices-based systems to ensure scalability, resiliency, and security.
Provide technical leadership in implementing multi-cloud strategies, focusing on developer services.
Drive adoption of data engineering and orchestration tools for distributed processing, Apache Kafka and Apache Flink for real-time data streaming, and Airflow/Prefect for workflow automation.
Oversee integration of machine learning solutions into production systems, leveraging TensorFlow and PyTorch for model development, training, optimization, and deployment.
Implement MLOps frameworks for end-to-end machine learning lifecycle management, including deployment, monitoring, and governance of models in production.
Implement service mesh technologies to secure and optimize microservices communication within Kubernetes environments.
Implement search and indexing technologies to support real-time data retrieval and high-performance search.
Collaborate with cross-functional teams (product managers, data scientists, infrastructure engineers) to align technology initiatives with business objectives.
Establish and enforce best practices in Agile SDLC, code quality, CI/CD automation, test-driven development, and observability (monitoring, logging, tracing).
Manage project budgets, timelines, and risks while ensuring compliance with regulatory and security standards.
Mentor and coach engineers to build technical expertise and leadership capability within the team.