Senior Machine Learning Engineer - LLM Models (2-7 yrs)

The ML/AI team at Client is a part of Engineering team, which has a mission to bring innovation and modernization to the auto-lending industry.

As a Senior MLE, you will play a pivotal role in the success of this mission as you would lead the development of AI-powered solutions across different business areas. This involves understanding the business processes, identifying new opportunities to add value using ML/AI algorithms and harnessing data sources to build state-of-the-art ML/AI solutions.

Outcomes and Activities :

- This position will work from home

- Identify and build the solutions for various GenAI problems and manage the end to end lifecycle from scoping and adaptation to application integration, monitoring and performance management

- Investigate the machine learning methodologies, including deep learning, LLM, and graph NN, to address diverse challenges across different business verticals and customers

- Build and deploy contextual Chatbots and analytical tools providing bespoke responses to internal and external customers across different platforms

- Develop LLM models trained and fine-tuned on internal multi modal data (ex: documents, policies, Pdfs, graphs, text, etc.) for a totally new set of problems

- Solve many open-ended problems as overall owner and foster a culture of widespread ML utilization

Competencies : The following items detail how you will be successful in this role.

- Customer Empathy : Customer Empathy is the ability to understand the perspectives, pain points, and experiences of customers. It involves actively putting oneself in the customer's shoes, comprehending their needs and challenges, and using that understanding to provide a better, more customer-centric experience.

- Engineering Excellence : Engineering Excellence is about bringing great craftsmanship and thought leadership to deliver an outstanding product that delights customers and solves for the business. This involves the pursuit and achievement of high standards, best practices, innovation, and superior solutions.

- One Team : A One Team mindset refers to a collaborative approach across the organization, where individuals work together seamlessly, without boundaries, as a single, cohesive team. Shared goals, open communication and mutual support create a sense of collective purpose. This enables teams to navigate challenges and pursue shared objectives more effectively.

- Owner's Mindset : Owner's Mindset involves adopting a set of behaviors that reflect a sense of responsibility, accountability, strategic thinking, and a proactive approach to managing your domain. As an owner, you understand the business and your domain(s) deeply and solve for the right outcome for the domain(s) and the business.

Requirements :

- PhD in Computer Science, Stats, Economics, or relevant technical field with at least 3+ years of relevant experience or MS with at least 3+ years of experience

- 2-7 years of experience building and deploying Deep Learning models including Reinforcement algorithms, Recommendation systems, etc. with solid understanding of the mathematics, advanced statistics and engineering behind building such infra

- Hands-on experience with building, fine-tuning and deploying multi-modal LLM Models and managing the end-to-end model lifecycle

- Experience partnering with the engineering, product, BizOps and other data teams while designing, building and executing solutions

- Deep understanding in at least three of the following areas: data mining, advanced statistics, machine learning, NLP or computer vision

- Strong problem solving with bias for action

Preferred :

- Demonstrable experience in parameter-efficient fine-tuning, model quantization, and quantization-aware fine-tuning of LLM models

- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies

- 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