JPMorgan Lead Software Engineer- Backend Engineer GenAI Cloud 

United States, California, Palo Alto 

232794407

Job Responsibilities:

• Design, develop, and maintain scalable and efficient backend systems and APIs.
• Develop and maintain APIs and microservices to facilitate AI model interactions.
• Optimize database structures and data pipelines for AI-driven workloads.
• Collaborate with frontend developers to integrate user-facing elements with server-side logic.
• Optimize applications for maximum speed and scalability.
• Implement security and data protection measures.
• Implement scalable cloud solutions for AI model deployment using platforms such as AWS, GCP, or Azure.
• Conduct code reviews, testing, and debugging to ensure high-quality code.
• Work with database technologies to design and manage data storage solutions.
• Stay up-to-date with the latest AI/ML research and backend engineering best practices.

Required Qualifications, Capabilities, and Skills:

• Formal training or certification in software engineering concepts with 5+ years of applied experience.
• Proven experience in backend development using languages such as Java, Go, or Python.
• Familiarity with authentication and authorization frameworks, including OAuth, JWT, SAML, and OPA (policy as code).
• Solid understanding of agile methodologies, including CI/CD, application resiliency, DevSecOps, and security.
• Knowledge of web protocols and standards, such as HTTP, REST, and gRPC, and cloud-native technologies like EnvoyProxy.
• Experience with cloud computing platforms, including AWS, Azure, or Google Cloud.
• Experience with Generative AI frameworks, such as TensorFlow, PyTorch, Hugging Face, or OpenAI APIs.
• Experience deploying and managing AI models on cloud platforms like AWS, GCP, or Azure.
• Proficiency in containerization and orchestration using Docker and Kubernetes.

Preferred Qualifications, Capabilities, and Skills:

• Experience with containerization technologies, including Docker and Kubernetes.
• Experience with fine-tuning large language models (LLMs) and optimizing inference.
• Experience with GraphQL.
• Understanding of LLM APIs, such as OpenAI, Cohere, Anthropic, or Meta AI.
• Familiarity with retrieval-augmented generation (RAG) and LangChain architectures.
• Familiarity with vector databases, such as FAISS, Pinecone, or Weaviate, is a plus.
• Experience with MLOps tools, such as MLflow or Kubeflow, is a plus.