JOB DESCRIPTIONWe have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Lead Software Engineer at JPMorganChase within the Consumer and community banking- Architecture & Engineering , you are an integral part of an agile team that works to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. As a core technical contributor, you are responsible for conducting critical technology solutions across multiple technical areas within various business functions in support of the firm’s business objectives.
Job responsibilities
Required qualifications, capabilities, and skills
- Formal training or certification on software engineering concepts and 5+ years of applied experience
- Hands-on practical experience delivering system design, application development, testing, and operational stability
- Advanced in one or more programming language(s) including Java/ Python (fastAPI), Microservices, API, LLM, and AWS (EC2, ECS, EKS, Lambda, SQS, SNS, RDS Aurora Oracle & Postgres, DynamoDB, S3)
- Develop innovative AI/ML solutions and agentic systems leveraging LLM on public cloud with modern standards, specifically with AWS, and AI Agentic frameworks
- Develop and implement state-of-the-art GenAI services leveraging Azure OpenAI models and AWS Bedrock service.
- Experience in designing and implementing pipelines using DAGs (e.g., Kubeflow, DVC, Ray) & Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints
- Ability to construct batch and streaming microservices exposed as gRPC and/or GraphQL endpoints & Knowledge in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models
- Understanding of large language model (LLM) approaches, such as Retrieval-Augmented Generation (RAG) and agent-based models, is essential.
Preferred qualifications, capabilities, and skills - Real-time model serving experience with Seldon, Ray, or AWS SM is a plus.
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG).
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.
- Good understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs is a plus.
ABOUT US
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