Role Overview:
We are seeking an Associate Director - NLP & GenAI to lead the design, development, and deployment of next-generation AI solutions. This is a senior technical role that requires deep expertise in Large Language Models (LLMs), NLP, and model fine-tuning, coupled with the ability to lead teams and scale solutions to production. You will innovate with advanced AI techniques, solve real-world problems, and shape the future of AI applications across industries.
Key Responsibilities:
- Building GenAI applications and solutions while addressing challenges like hallucinations, bias, and latency, and ensuring optimal performance and reliability
- LLM Agents & Intelligent Automation: Develop and integrate LLM agents with open-source and in-house tools to enable adaptive, AI-powered systems and workflows.
- End-to-End AI Solution Delivery: Lead the full lifecycle of NLP and GenAI projects, including ideation, model development, fine-tuning, deployment, and continuous monitoring to ensure reliable performance in production environments.
- Advanced LLM Fine-Tuning: Apply cutting-edge techniques such as LoRA, PEFT, and others to fine-tune LLMs for specific business use cases, driving meaningful outcomes as needed.
- Model Deployment & Scaling: Architect and manage scalable deployment pipelines, ensuring optimal performance through robust monitoring, error handling, and retraining strategies.
- Lead & Mentor: Provide strategic leadership and mentorship to a team of data scientists and AI engineers, fostering a culture of excellence, innovation, and collaboration.
What You'll Bring:
- Hands-On Leadership: Proven experience in hands-on development and leading teams of NLP and GenAI models, with a demonstrated ability to scale solutions in real-world environments.
- LLM Expertise: Deep understanding of LLM Models, their fine-tuning and the quantization methodologies.
- LLM Agents Knowledge: Practical experience building and deploying AI Agents using open-source frameworks
- Model Lifecycle Mastery: Expertise in deploying, managing, and monitoring machine learning models, with a strong focus on scaling and operationalizing them in production.
- AI Performance Optimization: Deep expertise in model evaluation, addressing hallucinations, biases, and other performance issues, ensuring continuous model improvement.
