Job description
Key points at a glance:
1. Company's vision - To provide a range of wealth-tech products supercharged with AI for a global userbase.
2. Traction - Series C funded company (about to raise Series D), global presence with users as well as offices across US and India. They have a portfolio of products and many of the products are already profitable. They are now launching new ones with a significant market scope and some large deals have already been cracked for these too.
3. Working on the latest tech - Build and deploy finetuned LLMs for the fintech usecase, while also leveraging RAG/CAG, Multi-Agent frameworks, Inference optimization, and more.
4. Tech leadership role - You will be one of the first few AI hires in India and will have the opportunity to lead the way for AI adoption for this product.
5. Startup environment but with large org stability - Given the company's traction, this role comes with significant stability while also providing the opportunity to work in small, fast-moving teams without unnecessary red tape.
Role Overview:
As the Lead AI Engineer/Staff Data Scientist, you will be responsible for designing, training, and deploying AI models, with a specific focus on finetuned LLMs and agent-based systems. You’ll work across the entire AI pipeline, from identifying use cases to deploying and monitoring models in production. This role is ideal for a technically skilled individual contributor who can independently drive innovation and deliver impactful solutions.
Key Responsibilities:
- Prototype Development: Build and validate prototypes by designing datasets and performing fine-tuning to demonstrate model feasibility and effectiveness.
- Model Finetuning: Lead the fine-tuning process for large language models, leveraging both open-source solutions (e.g., LLAMA, Deepseek) and proprietary models like GPT to address specific business needs.
- Retrieval-Augmented Generation (RAG): Design and optimize advanced RAG systems, implementing efficient methods for chunking, indexing, and managing complex document formats.
- Agentic Workflows: Develop and apply agentic workflows to create advanced conversation agents tailored for various use cases.
- Inference Frameworks: Utilize inference frameworks like VAM and advanced pipelines to improve scalability and streamline the deployment of machine learning models.
- ML Model Deployment: Lead the deployment and productionization of machine learning models, collaborating closely with software engineers to ensure seamless integration into products.
- Conversational Experiences: Work with cross-functional teams, including design and product, to craft engaging and personalized conversational AI experiences for end-users.
- Data Personalization: Leverage and analyze existing datasets to enhance model performance and create tailored user experiences, fine-tuning models to align with unique business cases.
- Research and Innovation: Drive innovative research aligned with business goals, exploring advancements in Natural Language Understanding (NLU) and conversational AI applications.
- Knowledge Sharing: Publish and present groundbreaking research in top-tier journals and conferences, contributing to the broader scientific community and enhancing organizational credibility.
Requirements:
- Experience: 7+ years in AI and machine learning, with at least 2+ years hands-on experience in NLP and GenAI. Must have finetuned and deployed LLMs in production.
- Technical Skills: Strong experience with large language models and understanding of pretraining/fine-tuning, prompt engineering, task adaptation, agentic frameworks, etc - basically all the skills needed to carry out the responsibilities given above.
- Communication Skills: Ability to clearly articulate technical concepts and AI-driven insights to non-technical stakeholders and document work for continuity.
- Startup Mindset: Take initiative in setting up workflows, tools, and systems from scratch, showcasing flexibility and adaptability in a growing organization.
- Leadership and Collaboration: Act as a leader and independent contributor, effectively collaborating across diverse teams while managing evolving project priorities.
- Independent Execution: Demonstrate the ability to independently manage projects, deliver features, and achieve outcomes in a dynamic, fast-paced startup environment.
Nice-to-Have:
- Advanced Degree: Master’s or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field.
- Knowledge of Financial AI Applications: Experience applying AI in the finance sector, understanding compliance, privacy, and domain-specific challenges.
- Familiarity with RL Techniques: Proficiency in reinforcement learning, especially with agentic system implementations.