Senior Manager of Software Engineering

Be an integral part of an agile team that's constantly pushing the envelope to enhance, build, and deliver top-notch technology products.

As a Senior Manager of Software Engineering at JPMorgan Chase within the Consumer and Community Bank Risk Feature Store, you will lead the engineering strategy and execution for our Consumer and Community Bank Risk Feature Store, delivering secure, high-quality, and observable data/ML feature services at scale. You will guide architecture, drive AI-for-Tech initiatives, and partner across Cloud, Data, and Application teams to enable reliable feature discovery, governance, and real-time serving for mission-critical ML workloads.

This position is ideal for someone passionate about solving business problems through innovative engineering practices. The team leverages a variety of cutting-edge tools like Databricks AI/ML and big data engineering on AWS to optimize feature engineering for critical business processes, including low latency and high throughput Feature/Model serving development through real time API solutions.

Job responsibilities:

• Provide hands-on technical guidance, architecture, and design direction to internal teams, contractors, and vendors, ensuring scalable, secure, and resilient feature pipelines and services; Own critical design decisions that shape product architecture, application functionality, technical operations, and SDLC processes across the Feature Store platform.

• Lead AI-for-Tech initiatives and drive CT CDAP and CMAT-aligned design and operational practices, ensuring compliance, efficiency, and reuse; Serve as a domain SME for one or more areas—feature management, real-time streaming, model-serving integration, feature life cycle management and observability.

   

  • Develop and review high-quality production code; set engineering quality bars through rigorous code reviews, testing, and secure coding standards.
  • Define and implement platform roadmaps, SLIs/SLOs, and reliability objectives for online/offline feature storage, transformation, lineage, and access control.
  • Partner with data scientists, ML engineers, and application teams to deliver APIs and SDKs that improve feature reuse, time-to-production, and model performance.
• Champion firmwide frameworks, tools, and SDLC best practices; influence peers and stakeholders to adopt modern patterns (microservices, Kubernetes, RESTful interfaces, API gateways).
• Promote innovation through proof-of-concepts and “out-of-the-box” solutions that balance velocity, safety, and operability.

Required qualifications, capabilities and skills:

• 5+ years of applied software engineering experience with formal training/certification; 2+ years leading technologists to solve complex technical problems within your domain.
• Proven delivery across system design, application development, automated testing, and operational stability in production environments.

• Practical cloud-native experience building and operating distributed systems at scale.

   

• Advanced proficiency in one or more programming languages and frameworks: Java, Python, Spring Boot, microservices, RESTful APIs, and Kubernetes, including AWS EMR, Databricks, Cassandra Real Time, AI/ML.
• Expertise integrating and operating API Gateways and event/streaming platforms (e.g., Apache Kafka), with data platforms such as Ops Data Store and Databricks or similar cloud data infrastructure.
• Advanced knowledge in one or more technical disciplines: cloud (public/private), artificial intelligence, machine learning, real time processing, or big data engineering.
• Demonstrated ability to independently frame and solve complex design and functionality challenges with minimal oversight.
• Thought leader who drives innovation through pragmatic experimentation and measurable outcomes.
• Excellent influencer capable of bringing stakeholders along with technology choices and process improvements.
• Demonstrated ability to mentor, coach, and grow engineering talent

Preferred qualifications, skills, and/or capabilities

• Experience building or operating feature stores or adjacent ML platform components (feature pipelines, metadata management, governance, online/offline stores).
• Familiarity with data governance, lineage, and access control patterns for regulated environments.
• Experience with schema evolution, low latency serving, and cost/performance trade-offs in real-time feature delivery.
• Hands-on experience with AWS cloud services (e.g. ECS, S3, Lambda) and proficiency in Infrastructure as Code (IaC) or Environment as Code (EaC).