Lead Software Engineer- SRE

Job Description
Job Description
We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.
As a Machine Learning and Python Software Engineer III at JPMorgan Chase within the Consumer and community banking technology team, you serve as a seasoned member of an agile team to design and deliver trusted market-leading technology products in a secure, stable, and scalable way. You are responsible for carrying out critical technology solutions across multiple technical areas within various business functions in support of the firm's business objectives. You will also be responsible for production stability of applications by providing solutions and implement mitigation actions.
Job responsibilities

• Development and deployment of AI/ML solutions for Client Service Function.
• Design, develop, and deploy state-of-the-art AI/ML/LLM/GenAI solutions to meet business objectives.
• Conduct thorough evaluations of Gen AI models, iterate on model architectures, and implement improvements to enhance overall performance across applications.
• Develop appropriate functional, non-functional, and performance testing frameworks for models.
• Guide and advise India team developers through Machine Learning Development Lifecycle (MDLC).
• Work directly with the US Core Client Service AI team on a daily basis to drive AI implementations from India.
• Stay updated with the latest trends and advancements in data science, machine learning, and related fields, and actively seek opportunities to enhance skills and knowledge.
• Collaborates with other software engineers and teams to design, develop, test, and implement availability, reliability, scalability, and solutions in their applications
• Implements infrastructure, configuration, and network as code for the applications and platforms in your remit
• Collaborates with technical experts, key stakeholders, and team members to resolve complex problems
• Understands service level indicators and utilizes service level objectives to proactively resolve issues before they impact customers

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Experience in programming skills like Python and Java
• Hands-on experience in applied AI/ML engineering with a track record of developing and deploying business-critical machine learning models and applications in production.
• Proficient in programming languages like Python for model development, experimentation, and integration with Azure OpenAI API.
• Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations, and fine-tune models for optimal performance in NLP applications.
• Strong collaboration and communication skills to work effectively with geographically spread cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects.
• Experience with cloud platforms, for deploying and scaling AI/ML models.
• Experience on AWS Cloud knowledge
• Ability to contribute to large and collaborative teams by presenting information in a logical and timely manner with compelling language and limited supervision
• Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages
• Demonstrated knowledge of software applications and technical processes within a technical discipline (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)

Preferred qualifications, capabilities, and skills

• Exposure databases (SQL/NoSQL/Graph), programming languages (Python/Java/Node.js), web frameworks, APIs, and microservices and possess front-end development skills.
• Knowledge of large language models (LLMs) and accompanying toolsets the LLM ecosystem (e.g. Lang chain, Vector databases, opensource Models like Mistral, Llama, etc)
• Exposure to cloud automation technologies such as Terraform
• Assess and choose suitable LLM tools and models for diverse tasks including but not limited to curating custom datasets and fine-tune LLM with a focus on parameter-efficient, mixture-of-expert, and instruction methods designing and developing advanced LLM prompts, Retrieval-Augmented Generation (RAG) solutions, and Intelligent agents for the LLMs and executing experiments to push the capability limits of LLM models and enhance their dependability.