Job responsibilities:
- Build AI enabled consumer centric and business use-case products using LLMs, RAG, Agents
- Design, develop software solutions, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or break down technical problems.
- Develops secure high-quality production code, and reviews and debugs code written by others.
- Develops innovative AI/ML solutions for the LLM Suite platform utilizing public cloud architecture and modern standards, specifically with Azure, AWS using both commercial and open source models
- Leads communities of practice across Software Engineering to drive awareness and use of new and leading-edge technologies.
- Influences leaders and senior stakeholders across business, product, and technology teams
- Creates durable, reusable software frameworks that are leveraged across teams and functions.
- Adds to team culture of diversity, equity, inclusion, and respect.
Required qualifications, capabilities, and skills:
- Formal training or certification onsoftware engineering and AI engineeringconcepts and 10+ years applied experience
- Hands on experience of Large Language Model (LLM) techniques, including Agents, Planning, Reasoning, and other related methods.
- Hands-on experience delivering system design, application development, testing, load testing, scaling, resiliency of AI Solutions
- Cloud native experience with AWS, Azure
- Proficient in building scalable, resilient, secure systems with AI models and distribute compute technologies.
- Ability to present and effectively communicate with Senior Leaders and Executives
- In-depth experience with /Ranking, Recommender systems, RAG (Similarity Search), Agent systems, and other advanced methodologies.
- Experience in Computer Science, Computer Engineering, Mathematics, or a related technical field.
Preferred qualifications, capabilities, and skills
- Experience with JAX, Ray, MLFlow, and/or other distributed training frameworks.
- Familiarity with NL 2SQL systems
- Experience with fine-tuning LLMs for specific usecases